arXiv Papers of Multi-Robot Systems

Abstract:
Cooperative perception through vehicle‑to‑everything (V2X) communication can overcome the inherent physical limitations of individual autonomous vehicles, such as occlusions and limited sensor range. However, the development of robust V2X algorithms, particularly those relying on unified spatial representations like bird's‑eye view (BEV) representation, is hampered by the lack of large‑scale, multi‑modal, multi‑task datasets. Moreover, collecting and annotating a large set of synchronized, real‑world multi‑agent data is prohibitively expensive. This has resulted in a landscape where existing V2X datasets are notably limited in both size and scope. To overcome this, we introduce SimBEV2X, an advanced synthetic data generation tool built on the CARLA simulator. SimBEV2X automatically creates randomized driving scenarios to collect multi‑modal sensor data alongside various types of ground truth including 3D bounding boxes with unique track IDs, HD map information, BEV segmentation maps, and semantic occupancy voxel grids from both vehicles and RSUs. We also present the SimBEV2X dataset, the largest V2X perception dataset to date. The dataset comprises 258 scenes, each involving up to 8 connected vehicles and up to 4 RSUs across a variety of road networks. The SimBEV2X dataset is an order of magnitude larger than existing V2X datasets and contains 102,200 frames, 588,520 lidar point clouds, more than 3 million images, over 27 million bounding boxes, and a comprehensive set of other annotations. Finally, we establish a strong baseline on the SimBEV2X dataset using CoopDet3D and propose CoBEVFusion, a novel architecture that combines CoopDet3D with fused axial attention (FAX) for context‑aware multi‑agent feature aggregation, resulting in superior performance. SimBEV2X, the SimBEV2X dataset, and CoBEVFusion are available at https://simbev2x.org and https://github.com/GoodarzMehr/SimBEV2X.

Abstract:
Collaborative‑perception enables multi‑robot systems to enhance situational awareness by sharing perceptual information. Existing collaborative‑perception systems face an inherent trade‑off between communication bandwidth requirements and perception accuracy, where methods that exchange more information achieve better perception results at the cost of increased communication overhead. However, real‑world communication networks impose bandwidth constraints that require minimizing communication overhead without sacrificing perception performance. To address this challenge, we propose HydraCollab, an adaptive collaborative‑perception framework that (i) selectively transmits the most informative sensor features and (ii) dynamically employs collaboration strategies (intermediate or late) based on spatial confidence maps. Extensive evaluations on the V2X‑R, V2X‑Radar and UAV3D‑mini datasets demonstrate that HydraCollab achieves the best overall trade‑off between accuracy and communication cost among existing collaborative‑perception methods. Relative to SOTA Where2comm, HydraCollab uses only 41% of the bandwidth on V2X‑R and 26% on V2X‑Radar while improving performance by 0.78% and 0.75% respectively. Our code and models are available at https://github.com/AICPS/HydraCollab.

Abstract:
LiDAR has increasingly been integrated into traffic cameras to expand coverage and mitigate occlusion in roadside cooperative perception. However, how unimodal and camera‑LiDAR fusion architectures behave under variations in LiDAR point sparsity induced by sensor configurations and scene‑dependent sensing conditions remains underexplored. We introduce RESOLVE, a large‑scale real‑world benchmark dataset featuring multi‑resolution roadside LiDAR and synchronized camera‑LiDAR sensing for systematic evaluation of unimodal and fusion‑based architectures in roadside 3D detection and tracking. RESOLVE contains over 100k images and 26k point cloud frames with 220k manually annotated bounding boxes, captured at a real‑world urban intersection across diverse lighting and weather conditions and spanning 10 classes of traffic participants. In particular, RESOLVE enables controlled evaluation across three LiDAR resolution levels while keeping all other sensing and environmental factors fixed. This allows fair cross‑architecture comparisons under point cloud distribution shifts resulting from resolution variations, sensing distance, and training‑inference resolution mismatches. Results from extensive benchmark experiments reveal insights into how multimodal fusion can compensate for LiDAR point sparsity, offering clues for designing cost‑efficient roadside multimodal perception. The dataset and benchmark codes are available at https://github.com/ASU‑Suo‑Lab/RESOLVE.

Abstract:
Coordinating heterogeneous multi‑robot systems (MRS) for complex, long‑horizon tasks requires both flexible high‑level reasoning and efficient low‑level scheduling. Existing LLM‑based approaches address the reasoning side but introduce two critical bottlenecks: (1) repeated LLM inference during execution, which inflates latency with agent count, and (2) offline, pre‑committed scheduling, which forces robots to idle while waiting for sequentially ordered predecessors even when independent work is available. This paper presents OSDAG, a novel framework that integrates LLM‑based task reasoning with Directed Acyclic Graph (DAG) representation and constraint‑aware online scheduling. The LLM is invoked once to decompose a natural‑language instruction into a dependency‑annotated task graph, and a lightweight online scheduler then allocates ready tasks to idle agents in real time. The DAG representation encodes both precedence and resource constraints, ensuring correctness while exposing all available parallelism. Experiments across five benchmark scenarios demonstrate that OSDAG achieves 5‑15x faster reasoning time compared to dialogue‑based methods, reduces makespan by up to 38% over sequential baselines, and maintains competitive success rates. Both simulation and real‑world experiments on dual‑arm manipulation tasks validate the effectiveness and practicality of the proposed approach for efficient multi‑robot coordination. The website and resources are available at http://thanhnguyencanh.github.io/LLM_DAG4MultiRobot

Abstract:
Cooperative perception enabled by Vehicle‑to‑Everything (V2X) communication enhances autonomous driving safety by creating a unified environmental representation through shared sensory data. While recent works have advanced multi‑agent fusion for improved perception, uncertainty quantification in such cooperative frameworks remains largely unexplored. This paper introduces Hyper‑V2X, a hypernetwork‑based framework for estimating both epistemic and aleatoric uncertainties in V2X‑based perception. Specifically, we propose a partial weight generation scheme and V2X context embedding module that conditions a Bayesian hypernetwork on fused multi‑agent features to generate weight distributions for stochastic Bird's‑Eye‑View (BEV) segmentation. Unlike existing deterministic BEV models, Hyper‑V2X enables efficient uncertainty estimation with little computation overhead. Our approach is architecture‑agnostic, and can be seamlessly integrating with modern cooperative backbones such as CoBEVT. Experiments on the OPV2V benchmark demonstrate that Hyper‑V2X provides accurate, well‑calibrated uncertainty estimates and improves overall perception reliability. Our code and benchmark are publicly available under an open‑source license: https://github.com/abhishekjagtap1/Hyper‑V2X

Abstract:
3D vision systems are fundamentally constrained by their reliance on visual overlap: reconstruction methods require it for geometric alignment, while generative models use it to enforce multi‑view consistency. This limitation is particularly acute in real‑world scenarios such as distributed swarm robotics or crowd‑sourced data collection, where capturing overlapping perspectives, both in terms of spatial and appearance overlap, is often impossible. We introduce Generative Reconstruction from Disjoint Views as a new paradigm, establish a comprehensive dataset, and propose specialized evaluation metrics for zero‑overlap scenarios. Our benchmarking demonstrates that existing state‑of‑the‑art methods fail catastrophically on this task, producing disconnected geometries or semantically incoherent reconstructions. To address these limitations, we propose GLADOS, a general, modular framework that operates through three stages: (1) Generative Bridging, where foundation models synthesize intermediate perspectives to connect disjoint inputs; (2) Robust Coarse 3D Reconstruction, that establish coarse geometric scaffold via global alignment which absorbs local contradictions from generative process; and (3) Iterative Context Expansion and Consistency Optimization to fill missing regions and unify the reconstruction. As an architectureagnostic framework, GLADOS enables seamless integration of future advances in generation, reconstruction, and inpainting. The source code is available at: https://github.com/gwilczynski95/GLADOS.

Abstract:
Most existing heterogeneous cooperative perception methods depend on prior preparation like offline joint training or tailored collaborator‑model adaptation. Such preprocessing is, however, generally impractical in real scenarios, as agents are usually independently trained by different developers and meet occasionally online. This work investigates \emphpreparation‑free heterogeneous cooperative perception, where agents use independently trained single‑agent detectors without any pre‑deployment coordination. We find direct cross‑agent fusion under this setting greatly underperforms ego‑only perception. We present BOLT, a lightweight plug‑and‑play module that adapts neighboring features online via ego‑as‑teacher distillation, requiring only ego predictions without ground‑truth labels. BOLT leverages high‑confidence ego perception features to guide cross‑agent feature‑domain alignment, while enabling neighbors to contribute features in the ego's low‑confidence regions. With only 0.9M trainable parameters, BOLT improves AP@50 by up to 32.3 points over vanilla unadapted fusion in the preparation‑free setting. It consistently outperforms ego‑only results on DAIR‑V2X and OPV2V, across different encoder pairs and fusion strategies. Code: https://github.com/sidiangongyuan/BOLT.

Abstract:
Multi‑robot control in cluttered environments is a challenging problem that involves complex physical constraints, including robot‑robot collisions, robot‑obstacle collisions, and unreachable motions. Successful planning in such settings requires joint optimization over high‑level task planning and low‑level motion planning, as violations of physical constraints may arise from failures at either level. However, jointly optimizing task and motion planning is difficult due to the complex parameterization of low‑level motion trajectories and the ambiguity of credit assignment across the two planning levels. In this paper, we propose a hybrid multi‑robot control framework that jointly optimizes task and motion planning. To enable effective parameterization of low‑level planning, we introduce waypoints, a simple yet expressive representation for motion trajectories. To address the credit assignment challenge, we adopt a curriculum‑based training strategy with a modified RLVR algorithm that propagates motion feasibility feedback from the motion planner to the task planner. Experiments on BoxNet3D‑OBS, a challenging multi‑robot benchmark with dense obstacles and up to nine robots, show that our approach consistently improves task success over motion‑agnostic and VLA‑based baselines. Our code is available at https://github.com/UCSB‑NLP‑Chang/navigate‑cluster

Abstract:
Multi‑robot systems hold significant promise for social environments such as homes and hospitals, yet existing multi‑robot works treat robots as functionally identical, overlooking how robots individual identity shape user perception and how coordination shapes multi‑robot behavior when such individuality is present. To address this, we introduce M2HRI, a multimodal multi‑agent framework built on large language models that equips each robot with distinct personality and long‑term memory, alongside a coordination mechanism conditioned on these differences. In a controlled user study (n = 105) in a multi‑agent human‑robot interaction (HRI) scenario, we find that LLM‑driven personality traits are significantly distinguishable and enhance interaction quality, long‑term memory improves personalization and preference awareness, and centralized coordination significantly reduces overlap while improving overall interaction quality. Together, these results demonstrate that both agent individuality and structured coordination are essential for coherent and socially appropriate multi‑agent HRI. Project website and code are available at https://project‑m2hri.github.io/.

Abstract:
Multi‑robot systems rely on underlying connectivity to ensure reliable communication and timely coordination. This paper studies the line‑of‑sight (LoS) connectivity maintenance problem in multi‑robot navigation with unknown obstacles. Prior works typically assume known environment maps to formulate LoS constraints between robots, which hinders their practical deployment. To overcome this limitation, we propose an inherently distributed approach where each robot only constructs an egocentric visible region based on its real‑time LiDAR scans, instead of endeavoring to build a global map online. The individual visible regions are shared through distributed communication to establish inter‑robot LoS constraints, which are then incorporated into a multi‑robot navigation framework to ensure LoS‑connectivity. Moreover, we enhance the robustness of connectivity maintenance by proposing a more accurate LoS‑distance metric, which further enables flexible topology optimization that eliminates redundant and effort‑demanding connections. The proposed framework is evaluated through extensive multi‑robot navigation and exploration tasks in both simulation and real‑world experiments. Results show that it reliably maintains LoS‑connectivity between robots in challenging environments cluttered with obstacles, even under large visible ranges and fragile minimal topologies, where existing methods consistently fail. Ablation studies also reveal that topology optimization boosts navigation efficiency by around 20%, demonstrating the framework's potential for efficient navigation under connectivity constraints.

Abstract:
Modern autonomous vehicle perception systems are often constrained by occlusions, blind spots, and limited sensing range. While existing cooperative perception paradigms, such as Vehicle‑to‑Vehicle (V2V) and Vehicle‑to‑Infrastructure (V2I), have demonstrated their effectiveness in mitigating these challenges, they remain limited to ground‑level collaboration and cannot fully address large‑scale occlusions or long‑range perception in complex environments. To advance research in cross‑view cooperative perception, we present V2U4Real, the first large‑scale real‑world multi‑modal dataset for Vehicle‑to‑UAV (V2U) cooperative object perception. V2U4Real is collected by a ground vehicle and a UAV equipped with multi‑view LiDARs and RGB cameras. The dataset covers urban streets, university campuses, and rural roads under diverse traffic scenarios, comprising over 56K LiDAR frames, 56K multi‑view camera images, and 700K annotated 3D bounding boxes across four classes. To support a wide range of research tasks, we establish benchmarks for single‑agent 3D object detection, cooperative 3D object detection, and object tracking. Comprehensive evaluations of several state‑of‑the‑art models demonstrate the effectiveness of V2U cooperation in enhancing perception robustness and long‑range awareness. The V2U4Real dataset and codebase is available at https://github.com/VjiaLi/V2U4Real.

Abstract:
We present Triple Zero Path Planning (TZPP), a collaborative framework for heterogeneous multi‑robot systems that requires zero training, zero prior knowledge, and zero simulation. TZPP employs a coordinator‑‑explorer architecture: a humanoid robot handles task coordination, while a quadruped robot explores and identifies feasible paths using guidance from a multimodal large language model. We implement TZPP on Unitree G1 and Go2 robots and evaluate it across diverse indoor and outdoor environments, including obstacle‑rich and landmark‑sparse settings. Experiments show that TZPP achieves robust, human‑comparable efficiency and strong adaptability to unseen scenarios. By eliminating reliance on training and simulation, TZPP offers a practical path toward real‑world deployment of heterogeneous robot cooperation. Our code and video are provided at: https://github.com/triple‑zeropp/Triple‑zero‑robot‑agent

Abstract:
This paper considers the perception safety problem in distributed vision‑based leader‑follower formations, where each robot uses onboard perception to estimate relative states, track desired setpoints, and keep the leader within its camera field of view (FOV). Safety is challenging due to heteroscedastic perception errors and the coupling between formation maneuvers and visibility constraints. We propose a distributed, formation‑aware adaptive conformal prediction method based on Risk‑Aware Mondrian CP to produce formation‑conditioned uncertainty quantiles. The resulting bounds tighten in high‑risk configurations (near FOV limits) and relax in safer regions. We integrate these bounds into a Formation‑Aware Conformal CBF‑QP with a smooth margin to enforce visibility while maintaining feasibility and tracking performance. Gazebo simulations show improved formation success rates and tracking accuracy over non‑adaptive (global) CP baselines that ignore formation‑dependent visibility risk, while preserving finite‑sample probabilistic safety guarantees. The experimental videos are available on the \hrefhttps://nail‑uh.github.io/iros2026.github.io/project website\footnoteProject Website: https://nail‑uh.github.io/iros2026.github.io/.

Abstract:
Multi‑Agent Path Finding (MAPF) remains a fundamental challenge in robotics, where classical centralized approaches exhibit exponential growth in joint‑state complexity as the number of agents increases. This paper investigates Quadratic Unconstrained Binary Optimization (QUBO) as a structurally scalable alternative for simultaneous multi‑robot path planning. This approach is a robotics‑oriented QUBO formulation incorporating BFS‑based logical pre‑processing (achieving over 95% variable reduction), adaptive penalty design for collision and constraint enforcement, and a time‑windowed decomposition strategy that enables execution within current hardware limitations. An experimental evaluation in grid environments with up to four robots demonstrated near‑optimal solutions in dense scenarios and favorable scaling behavior compared to sequential classical planning. These results establish a practical and reproducible baseline for future quantum and quantum‑inspired multi‑robot coordinations.

Abstract:
Cooperative perception is critical for autonomous driving, overcoming the inherent limitations of a single vehicle, such as occlusions and constrained fields‑of‑view. However, current approaches sharing dense Bird's‑Eye‑View (BEV) features are constrained by quadratically‑scaling communication costs and the lack of flexibility and interpretability for precise alignment across asynchronous or disparate viewpoints. While emerging sparse query‑based methods offer an alternative, they often suffer from inadequate geometric representations, suboptimal fusion strategies, and training instability. In this paper, we propose SparseCoop, a fully sparse cooperative perception framework for 3D detection and tracking that completely discards intermediate BEV representations. Our framework features a trio of innovations: a kinematic‑grounded instance query that uses an explicit state vector with 3D geometry and velocity for precise spatio‑temporal alignment; a coarse‑to‑fine aggregation module for robust fusion; and a cooperative instance denoising task to accelerate and stabilize training. Experiments on V2X‑Seq and Griffin datasets show SparseCoop achieves state‑of‑the‑art performance. Notably, it delivers this with superior computational efficiency, low transmission cost, and strong robustness to communication latency. Code is available at https://github.com/wang‑jh18‑SVM/SparseCoop.

Abstract:
We present X‑SYCON, a xylem‑inspired multi‑agent architecture in which coordination emerges from passive field dynamics rather than explicit planning or communication. Incidents (demands) and obstructions (hazards) continually write diffusing and decaying scalar fields, and agents greedily ascend a local utility U=ϕ_\mathrmDE‑κ\,ϕ_\mathrmHZ with light anti‑congestion and separation. A beaconing rule triggered on first contact temporarily deepens the local demand sink, accelerating completion without reducing time‑to‑first‑response. Across dynamic, partially blocked simulated environments, we observe low miss rates and stable throughput with interpretable, tunable trade‑offs over carrier count, arrival rate, hazard density, and hazard sensitivity κ. We derive that a characteristic hydraulic length scale \ell\approx\sqrtD/λ predicts recruitment range in a continuum approximation, and we provide a work‑conservation (Ohm‑law) bound consistent with sublinear capacity scaling with team size. Empirically: (i) soft hazard penalties yield fewer misses when obstacles already block motion; (ii) throughput saturates sublinearly with carriers while reliability improves sharply; (iii) stronger arrivals can reduce misses by sustaining sinks that recruit help; and (iv) phase‑stability regions shrink with hazard density but are recovered by more carriers or higher arrivals. We refer to X‑SYCON as an instance of Distributed Passive Computation and Control, and we evaluate it in simulations modeling communication‑denied disaster response and other constrained sensing‑action regimes.

Abstract:
Efficient, accurate, and flexible relative localization is crucial in air‑ground collaborative tasks. However, current approaches for robot relative localization are primarily realized in the form of distributed multi‑robot SLAM systems with the same sensor configuration, which are tightly coupled with the state estimation of all robots, limiting both flexibility and accuracy. To this end, we fully leverage the high capacity of Unmanned Ground Vehicle (UGV) to integrate multiple sensors, enabling a semi‑distributed cross‑modal air‑ground relative localization framework. In this work, both the UGV and the Unmanned Aerial Vehicle (UAV) independently perform SLAM while extracting deep learning‑based keypoints and global descriptors, which decouples the relative localization from the state estimation of all agents. The UGV employs a local Bundle Adjustment (BA) with LiDAR, camera, and an IMU to rapidly obtain accurate relative pose estimates. The BA process adopts sparse keypoint optimization and is divided into two stages: First, optimizing camera poses interpolated from LiDAR‑Inertial Odometry (LIO), followed by estimating the relative camera poses between the UGV and UAV. Additionally, we implement an incremental loop closure detection algorithm using deep learning‑based descriptors to maintain and retrieve keyframes efficiently. Experimental results demonstrate that our method achieves outstanding performance in both accuracy and efficiency. Unlike traditional multi‑robot SLAM approaches that transmit images or point clouds, our method only transmits keypoint pixels and their descriptors, effectively constraining the communication bandwidth under 0.3 Mbps. Codes and data will be publicly available on https://github.com/Ascbpiac/cross‑model‑relative‑localization.git.

Abstract:
Recent cooperative perception datasets have played a crucial role in advancing smart mobility applications by enabling information exchange between intelligent agents, helping to overcome challenges such as occlusions and improving overall scene understanding. While some existing real‑world datasets incorporate both vehicle‑to‑vehicle and vehicle‑to‑infrastructure interactions, they are typically limited to a single intersection or a single vehicle. A comprehensive perception dataset featuring multiple connected vehicles and infrastructure sensors across several intersections remains unavailable, limiting the benchmarking of algorithms in diverse traffic environments. Consequently, overfitting can occur, and models may demonstrate misleadingly high performance due to similar intersection layouts and traffic participant behavior. To address this gap, we introduce UrbanIng‑V2X, the first large‑scale, multi‑modal dataset supporting cooperative perception involving vehicles and infrastructure sensors deployed across three urban intersections in Ingolstadt, Germany. UrbanIng‑V2X consists of 34 temporally aligned and spatially calibrated sensor sequences, each lasting 20 seconds. All sequences contain recordings from one of three intersections, involving two vehicles and up to three infrastructure‑mounted sensor poles operating in coordinated scenarios. In total, UrbanIng‑V2X provides data from 12 vehicle‑mounted RGB cameras, 2 vehicle LiDARs, 17 infrastructure thermal cameras, and 12 infrastructure LiDARs. All sequences are annotated at a frequency of 10 Hz with 3D bounding boxes spanning 13 object classes, resulting in approximately 712k annotated instances across the dataset. We provide comprehensive evaluations using state‑of‑the‑art cooperative perception methods and publicly release the codebase, dataset, HD map, and a digital twin of the complete data collection environment.

Abstract:
We consider the distributed pose‑graph optimization (PGO) problem, which is fundamental in accurate trajectory estimation in multi‑robot simultaneous localization and mapping (SLAM). Conventional iterative approaches linearize a highly non‑convex optimization objective, requiring repeated solving of normal equations, which often converge to local minima and thus produce suboptimal estimates. We propose a scalable, outlier‑robust distributed planar PGO framework using Multi‑Agent Reinforcement Learning (MARL). We cast distributed PGO as a partially observable Markov game defined on local pose‑graphs, where each action refines a single edge's pose estimate. A graph partitioner decomposes the global pose graph, and each robot runs a recurrent edge‑conditioned Graph Neural Network (GNN) encoder with adaptive edge‑gating to denoise noisy edges. Robots sequentially refine poses through a hybrid policy that utilizes prior action memory and graph embeddings. After local graph correction, a consensus scheme reconciles inter‑robot disagreements to produce a globally consistent estimate. Our extensive evaluations on a comprehensive suite of synthetic and real‑world datasets demonstrate that our learned MARL‑based actors reduce the global objective by an average of 37.5% more than the state‑of‑the‑art distributed PGO framework, while enhancing inference efficiency by at least 6X. We also demonstrate that actor replication allows a single learned policy to scale effortlessly to substantially larger robot teams without any retraining. Code is publicly available at https://github.com/herolab‑uga/policies‑over‑poses.

Abstract:
Coordinating heterogeneous robot fleets to achieve multiple goals is challenging in multi‑robot systems. We introduce an open‑source and extensible framework for centralized multi‑robot task planning and scheduling that leverages LLMs to enable fleets of heterogeneous robots to accomplish multiple tasks. RobotFleet provides abstractions for planning, scheduling, and execution across robots deployed as containerized services to simplify fleet scaling and management. The framework maintains a shared declarative world state and two‑way communication for task execution and replanning. By modularizing each layer of the autonomy stack and using LLMs for open‑world reasoning, RobotFleet lowers the barrier to building scalable multi‑robot systems. The code can be found here: https://github.com/therohangupta/robot‑fleet.

Abstract:
Current state‑of‑the‑art autonomous vehicles could face safety‑critical situations when their local sensors are occluded by large nearby objects on the road. Vehicle‑to‑vehicle (V2V) cooperative autonomous driving has been proposed as a means of addressing this problem, and one recently introduced framework for cooperative autonomous driving has further adopted an approach that incorporates a Multimodal Large Language Model (MLLM) to integrate cooperative perception and planning processes. However, despite the potential benefit of applying graph‑of‑thoughts reasoning to the MLLM, this idea has not been considered by previous cooperative autonomous driving research. In this paper, we propose a novel graph‑of‑thoughts framework specifically designed for MLLM‑based cooperative autonomous driving. Our graph‑of‑thoughts includes our proposed novel ideas of occlusion‑aware perception and planning‑aware prediction. We curate the V2V‑GoT‑QA dataset and develop the V2V‑GoT model for training and testing the cooperative driving graph‑of‑thoughts. Our experimental results show that our method outperforms other baselines in cooperative perception, prediction, and planning tasks. Our project website: https://eddyhkchiu.github.io/v2vgot.github.io/ .

Abstract:
Model‑based reinforcement learning (MBRL) has achieved remarkable success in robotics due to its high sample efficiency and planning capability. However, extending MBRL to physical multi‑robot cooperation remains challenging due to the complexity of joint dynamics. To address this challenge, we propose the Sequential World Model (SeqWM), a novel framework that integrates the sequential paradigm into multi‑robot MBRL. SeqWM employs independent, autoregressive agent‑wise world models to represent joint dynamics, where each agent generates its future trajectory and plans its actions based on the predictions of its predecessors. This design lowers modeling complexity and enables the emergence of advanced cooperative behaviors through explicit intention sharing. Experiments on Bi‑DexHands and Multi‑Quadruped demonstrate that SeqWM outperforms existing state‑of‑the‑art model‑based and model‑free baselines in both overall performance and sample efficiency, while exhibiting advanced cooperative behaviors such as predictive adaptation, temporal alignment, and role division. Furthermore, SeqWM has been successfully deployed on physical quadruped robots, validating its effectiveness in real‑world multi‑robot systems. Demos and code are available at: https://github.com/zhaozijie2022/seqwm

Abstract:
Cooperative perception through Vehicle‑to‑Everything (V2X) communication offers significant potential for enhancing vehicle perception by mitigating occlusions and expanding the field of view. However, past research has predominantly focused on improving accuracy metrics without addressing the crucial system‑level considerations of efficiency, latency, and real‑world deployability. Noticeably, most existing systems rely on full‑precision models, which incur high computational and transmission costs, making them impractical for real‑time operation in resource‑constrained environments. In this paper, we introduce QuantV2X, the first fully quantized multi‑agent system designed specifically for efficient and scalable deployment of multi‑modal, multi‑agent V2X cooperative perception. QuantV2X introduces a unified end‑to‑end quantization strategy across both neural network models and transmitted message representations that simultaneously reduces computational load and transmission bandwidth. Remarkably, despite operating under low‑bit constraints, QuantV2X achieves accuracy comparable to full‑precision systems. More importantly, when evaluated under deployment‑oriented metrics, QuantV2X reduces system‑level latency by 3.2× and achieves a +9.5 improvement in mAP30 over full‑precision baselines. Furthermore, QuantV2X scales more effectively, enabling larger and more capable models to fit within strict memory budgets. These results highlight the viability of a fully quantized multi‑agent intermediate fusion system for real‑world deployment. The system will be publicly released to promote research in this field: https://github.com/ucla‑mobility/QuantV2X.

Abstract:
Online coordination of multi‑robot systems in open and unknown environments faces significant challenges, particularly when semantic features detected during operation dynamically trigger new tasks. Recent large language model (LLMs)‑based approaches for scene reasoning and planning primarily focus on one‑shot, end‑to‑end solutions in known environments, lacking both dynamic adaptation capabilities for online operation and explainability in the processes of planning. To address these issues, a novel framework (DEXTER‑LLM) for dynamic task planning in unknown environments, integrates four modules: (i) a mission comprehension module that resolves partial ordering of tasks specified by natural languages or linear temporal logic formulas (LTL); (ii) an online subtask generator based on LLMs that improves the accuracy and explainability of task decomposition via multi‑stage reasoning; (iii) an optimal subtask assigner and scheduler that allocates subtasks to robots via search‑based optimization; and (iv) a dynamic adaptation and human‑in‑the‑loop verification module that implements multi‑rate, event‑based updates for both subtasks and their assignments, to cope with new features and tasks detected online. The framework effectively combines LLMs' open‑world reasoning capabilities with the optimality of model‑based assignment methods, simultaneously addressing the critical issue of online adaptability and explainability. Experimental evaluations demonstrate exceptional performances, with 100% success rates across all scenarios, 160 tasks and 480 subtasks completed on average (3 times the baselines), 62% less queries to LLMs during adaptation, and superior plan quality (2 times higher) for compound tasks. Project page at https://tcxm.github.io/DEXTER‑LLM/

Abstract:
Simulation is central to the evaluation of intelligent transportation system (ITS) applications. As ITS increasingly incorporates autonomous vehicle (AV) technologies as fleet vehicles and/or mobile sensors, accurate modeling of their perception capabilities becomes essential in high‑fidelity simulations. While game‑engine‑based simulators reproduce realistic perception environments through 3D scene rendering and raw sensor data generation, they face scalability challenges in simulating traffic networks with a large number of AVs due to high computational cost. In contrast, microscopic traffic simulators (MTS) can scale efficiently but lack perception modeling capabilities. To bridge this gap, we propose MIDAR, a surrogate LiDAR detection model that mimics realistic LiDAR detections using only high‑level features readily available from MTS. Specifically, MIDAR predicts true‑positive and false‑negative LiDAR detections based on the relative positions and dimensions of surrounding objects. To capture LiDAR visibility and occlusion effects, MIDAR introduces a ray‑hit feature and a Refined Multi‑hop Line‑of‑Sight (RM‑LoS) graph processed by a geometry‑aware Graph Transformer. MIDAR achieves an AUC of 0.94 in approximating LiDAR detection results using CARLA‑generated point cloud data, and an AUC of 0.86 with real‑world data from the nuScenes dataset. Two ITS applications, cooperative‑perception‑based adaptive signal control and vehicle trajectory reconstruction, are integrated with MIDAR to further validate its realism and necessity. Results show that MIDAR generates more realistic detection outputs as well as application‑level performance metrics than simplified perception models while introducing minimal computational overhead, enabling seamless integration into large‑scale, real‑time traffic simulations. The code and data are publicly available at https://github.com/Purdue‑CART‑Lab/MIDAR.

Abstract:
Cooperative perception aims to address the inherent limitations of single‑vehicle autonomous driving systems through information exchange among multiple agents. Previous research has primarily focused on single‑frame perception tasks. However, the more challenging cooperative sequential perception tasks, such as cooperative 3D multi‑object tracking, have not been thoroughly investigated. Therefore, we propose CoopTrack, a fully instance‑level end‑to‑end framework for cooperative tracking, featuring learnable instance association, which fundamentally differs from existing approaches. CoopTrack transmits sparse instance‑level features that significantly enhance perception capabilities while maintaining low transmission costs. Furthermore, the framework comprises two key components: Multi‑Dimensional Feature Extraction, and Cross‑Agent Association and Aggregation, which collectively enable comprehensive instance representation with semantic and motion features, and adaptive cross‑agent association and fusion based on a feature graph. Experiments on both the V2X‑Seq and Griffin datasets demonstrate that CoopTrack achieves excellent performance. Specifically, it attains state‑of‑the‑art results on V2X‑Seq, with 39.0% mAP and 32.8% AMOTA. The project is available at https://github.com/zhongjiaru/CoopTrack.

Abstract:
Multi‑UAV Coverage Path Planning (mCPP) algorithms in popular commercial software typically treat a Region of Interest (RoI) only as a 2D plane, ignoring important3D structure characteristics. This leads to incomplete 3Dreconstructions, especially around occluded or vertical surfaces. In this paper, we propose a modular algorithm that can extend commercial two‑dimensional path planners to facilitate terrain‑aware planning by adjusting altitude and camera orientations. To demonstrate it, we extend the well‑known DARP (Divide Areas for Optimal Multi‑Robot Coverage Path Planning) algorithm and produce DARP‑3D. We present simulation results in multiple 3D environments and a real‑world flight test using DJI hardware. Compared to baseline, our approach consistently captures improved 3D reconstructions, particularly in areas with significant vertical features. An open‑source implementation of the algorithm is available here:https://github.com/konskara/TerraPlan

Abstract:
This paper presents a novel spherical target‑based LiDAR‑camera extrinsic calibration method designed for outdoor environments with multi‑robot systems, considering both target and sensor corruption. The method extracts the 2D ellipse center from the image and the 3D sphere center from the pointcloud, which are then paired to compute the transformation matrix. Specifically, the image is first decomposed using the Segment Anything Model (SAM). Then, a novel algorithm extracts an ellipse from a potentially corrupted sphere, and the extracted center of ellipse is corrected for errors caused by the perspective projection model. For the LiDAR pointcloud, points on the sphere tend to be highly noisy due to the absence of flat regions. To accurately extract the sphere from these noisy measurements, we apply a hierarchical weighted sum to the accumulated pointcloud. Through experiments, we demonstrated that the sphere can be robustly detected even under both types of corruption, outperforming other targets. We evaluated our method using three different types of LiDARs (spinning, solid‑state, and non‑repetitive) with cameras positioned in three different locations. Furthermore, we validated the robustness of our method to target corruption by experimenting with spheres subjected to various types of degradation. These experiments were conducted in both a planetary test and a field environment. Our code is available at https://github.com/sparolab/MARSCalib.

Abstract:
Relative localization is a crucial capability for multi‑robot systems operating in GPS‑denied environments. Existing approaches for multi‑robot relative localization often depend on costly or short‑range sensors like cameras and LiDARs. Consequently, these approaches face challenges such as high computational overhead (e.g., map merging) and difficulties in disjoint environments. To address this limitation, this paper introduces MGPRL, a novel distributed framework for multi‑robot relative localization using convex‑hull of multiple Wi‑Fi access points (AP). To accomplish this, we employ co‑regionalized multi‑output Gaussian Processes for efficient Radio Signal Strength Indicator (RSSI) field prediction and perform uncertainty‑aware multi‑AP localization, which is further coupled with weighted convex hull‑based alignment for robust relative pose estimation. Each robot predicts the RSSI field of the environment by an online scan of APs in its environment, which are utilized for position estimation of multiple APs. To perform relative localization, each robot aligns the convex hull of its predicted AP locations with that of the neighbor robots. This approach is well‑suited for devices with limited computational resources and operates solely on widely available Wi‑Fi RSSI measurements without necessitating any dedicated pre‑calibration or offline fingerprinting. We rigorously evaluate the performance of the proposed MGPRL in ROS simulations and demonstrate it with real‑world experiments, comparing it against multiple state‑of‑the‑art approaches. The results showcase that MGPRL outperforms existing methods in terms of localization accuracy and computational efficiency. Finally, we open source MGPRL as a ROS package https://github.com/herolab‑uga/MGPRL.

Abstract:
Although Multi‑Agent Reinforcement Learning (MARL) is effective for complex multi‑robot tasks, it suffers from low sample efficiency and requires iterative manual reward tuning. Large Language Models (LLMs) have shown promise in single‑robot settings, but their application in multi‑robot systems remains largely unexplored. This paper introduces a novel LLM‑Aided MARL (LAMARL) approach, which integrates MARL with LLMs, significantly enhancing sample efficiency without requiring manual design. LAMARL consists of two modules: the first module leverages LLMs to fully automate the generation of prior policy and reward functions. The second module is MARL, which uses the generated functions to guide robot policy training effectively. On a shape assembly benchmark, both simulation and real‑world experiments demonstrate the unique advantages of LAMARL. Ablation studies show that the prior policy improves sample efficiency by an average of 185.9% and enhances task completion, while structured prompts based on Chain‑of‑Thought (CoT) and basic APIs improve LLM output success rates by 28.5%‑67.5%. Videos and code are available at https://windylab.github.io/LAMARL/

Abstract:
Multi‑robot systems (MRSs) are valuable for tasks such as search and rescue due to their ability to coordinate over shared observations. A central challenge in these systems is aligning independently collected perception data across space and time, i.e., multi‑robot data association. While recent advances in collaborative SLAM (C‑SLAM), map merging, and inter‑robot loop closure detection have significantly progressed the field, evaluation strategies still predominantly rely on splitting a single trajectory from single‑robot SLAM datasets into multiple segments to simulate multiple robots. Without careful consideration to how a single trajectory is split, this approach will fail to capture realistic pose‑dependent variation in observations of a scene inherent to multi‑robot systems. To address this gap, we present CU‑Multi, a multi‑robot dataset collected over multiple days at two locations on the University of Colorado Boulder campus. Using a single robotic platform, we generate four synchronized runs with aligned start times and deliberate percentages of trajectory overlap. CU‑Multi includes RGB‑D, GPS with accurate geospatial heading, and semantically annotated LiDAR data. By introducing controlled variations in trajectory overlap and dense lidar annotations, CU‑Multi offers a compelling alternative for evaluating methods in multi‑robot data association. Instructions on accessing the dataset, support code, and the latest updates are publicly available at https://arpg.github.io/cumulti

Abstract:
Distributed LiDAR SLAM is crucial for achieving efficient robot autonomy and improving the scalability of mapping. However, two issues need to be considered when applying it in field environments: one is resource limitation, and the other is inter/intra‑robot association. The resource limitation issue arises when the data size exceeds the processing capacity of the network or memory, especially when utilizing communication systems or onboard computers in the field. The inter/intra‑robot association issue occurs due to the narrow convergence region of ICP under large viewpoint differences, triggering many false positive loops and ultimately resulting in an inconsistent global map for multi‑robot systems. To tackle these problems, we propose a distributed LiDAR SLAM framework designed for versatile field applications, called SKiD‑SLAM. Extending our previous work that solely focused on lightweight place recognition and fast and robust global registration, we present a multi‑robot mapping framework that focuses on robust and lightweight inter‑robot loop closure in distributed LiDAR SLAM. Through various environmental experiments, we demonstrate that our method is more robust and lightweight compared to other state‑of‑the‑art distributed SLAM approaches, overcoming resource limitation and inter/intra‑robot association issues. Also, we validated the field applicability of our approach through mapping experiments in real‑world planetary emulation terrain and cave environments, which are in‑house datasets. Our code will be available at https://sparolab.github.io/research/skid_slam/.

Abstract:
Multi‑agent reinforcement learning (MARL) has emerged as a promising solution for learning complex and scalable coordination behaviors in multi‑robot systems. However, established MARL platforms (e.g., SMAC and MPE) lack robotics relevance and hardware deployment, leaving multi‑robot learning researchers to develop bespoke environments and hardware testbeds dedicated to the development and evaluation of their individual contributions. The Multi‑Agent RL Benchmark and Learning Environment for the Robotarium (MARBLER) is an exciting recent step in providing a standardized robotics‑relevant platform for MARL, by bridging the Robotarium testbed with existing MARL software infrastructure. However, MARBLER lacks support for parallelization and GPU/TPU execution, making the platform prohibitively slow compared to modern MARL environments and hindering adoption. We contribute JaxRobotarium, a Jax‑powered end‑to‑end simulation, learning, deployment, and benchmarking platform for the Robotarium. JaxRobotarium enables rapid training and deployment of multi‑robot RL (MRRL) policies with realistic robot dynamics and safety constraints, supporting parallelization and hardware acceleration. Our generalizable learning interface integrates easily with SOTA MARL libraries (e.g., JaxMARL). In addition, JaxRobotarium includes eight standardized coordination scenarios, including four novel scenarios that bring established MARL benchmark tasks (e.g., RWARE and Level‑Based Foraging) to a robotics setting. We demonstrate that JaxRobotarium retains high simulation fidelity while achieving dramatic speedups over baseline (20x in training and 150x in simulation), and provides an open‑access sim‑to‑real evaluation pipeline through the Robotarium testbed, accelerating and democratizing access to multi‑robot learning research and evaluation. Our code is available at https://github.com/GT‑STAR‑Lab/JaxRobotarium.

Abstract:
We introduce M^3CAD, a comprehensive benchmark designed to advance research in generic cooperative autonomous driving. M^3CAD comprises 204 sequences with 30,000 frames. Each sequence includes data from multiple vehicles and different types of sensors, e.g., LiDAR point clouds, RGB images, and GPS/IMU, supporting a variety of autonomous driving tasks, including object detection and tracking, mapping, motion forecasting, occupancy prediction, and path planning. This rich multimodal setup enables M^3CAD to support both single‑vehicle and multi‑vehicle cooperative autonomous driving research. To the best of our knowledge, M^3CAD is the most complete benchmark specifically designed for cooperative, multi‑task autonomous driving research. To test its effectiveness, we use M^3CAD to evaluate both state‑of‑the‑art single‑vehicle and cooperative driving solutions, setting baseline performance results. Since most existing cooperative perception methods focus on merging features but often ignore network bandwidth requirements, we propose a new multi‑level fusion approach which adaptively balances communication efficiency and perception accuracy based on the current network conditions. We release M^3CAD, along with the baseline models and evaluation results, to support the development of robust cooperative autonomous driving systems. All resources will be made publicly available on https://github.com/zhumorui/M3CAD

Abstract:
The development of control policies for multi‑robot systems traditionally follows a complex and labor‑intensive process, often lacking the flexibility to adapt to dynamic tasks. This has motivated research on methods to automatically create control policies. However, these methods require iterative processes of manually crafting and refining objective functions, thereby prolonging the development cycle. This work introduces GenSwarm, an end‑to‑end system that leverages large language models to automatically generate and deploy control policies for multi‑robot tasks based on simple user instructions in natural language. As a multi‑language‑agent system, GenSwarm achieves zero‑shot learning, enabling rapid adaptation to altered or unseen tasks. The white‑box nature of the code policies ensures strong reproducibility and interpretability. With its scalable software and hardware architectures, GenSwarm supports efficient policy deployment on both simulated and real‑world multi‑robot systems, realizing an instruction‑to‑execution end‑to‑end functionality that could prove valuable for robotics specialists and non‑specialists alike.The code of the proposed GenSwarm system is available online: https://github.com/WindyLab/GenSwarm.

Abstract:
Pursuit‑evasion (PE) problem is a critical challenge in multi‑robot systems (MRS). While reinforcement learning (RL) has shown its promise in addressing PE tasks, research has primarily focused on single‑target pursuit, with limited exploration of multi‑target encirclement, particularly in large‑scale settings. This paper proposes a Transformer‑Enhanced Reinforcement Learning (TERL) framework for large‑scale multi‑target encirclement. By integrating a transformer‑based policy network with target selection, TERL enables robots to adaptively prioritize targets and safely coordinate robots. Results show that TERL outperforms existing RL‑based methods in terms of encirclement success rate and task completion time, while maintaining good performance in large‑scale scenarios. Notably, TERL, trained on small‑scale scenarios (15 pursuers, 4 targets), generalizes effectively to large‑scale settings (80 pursuers, 20 targets) without retraining, achieving a 100% success rate. The code and demonstration video are available at https://github.com/ApricityZ/TERL.

Abstract:
Recent advances in Large Language Models (LLMs) have permitted the development of language‑guided multi‑robot systems, which allow robots to execute tasks based on natural language instructions. However, achieving effective coordination in distributed multi‑agent environments remains challenging due to (1) misalignment between instructions and task requirements and (2) inconsistency in robot behaviors when they independently interpret ambiguous instructions. To address these challenges, we propose Instruction‑Conditioned Coordinator (ICCO), a Multi‑Agent Reinforcement Learning (MARL) framework designed to enhance coordination in language‑guided multi‑robot systems. ICCO consists of a Coordinator agent and multiple Local Agents, where the Coordinator generates Task‑Aligned and Consistent Instructions (TACI) by integrating language instructions with environmental states, ensuring task alignment and behavioral consistency. The Coordinator and Local Agents are jointly trained to optimize a reward function that balances task efficiency and instruction following. A Consistency Enhancement Term is added to the learning objective to maximize mutual information between instructions and robot behaviors, further improving coordination. Simulation and real‑world experiments validate the effectiveness of ICCO in achieving language‑guided task‑aligned multi‑robot control. The demonstration can be found at https://yanoyoshiki.github.io/ICCO/.

Abstract:
Navigating unknown three‑dimensional (3D) rugged environments is challenging for multi‑robot systems. Traditional discrete systems struggle with rough terrain due to limited individual mobility, while modular systems‑‑where rigid, controllable constraints link robot units‑‑improve traversal but suffer from high control complexity and reduced flexibility. To address these limitations, we propose the Multi‑Robot System with Controllable Weak Constraints (MRS‑CWC), where robot units are connected by constraints with dynamically adjustable stiffness. This adaptive mechanism softens or stiffens in real‑time during environmental interactions, ensuring a balance between flexibility and mobility. We formulate the system's dynamics and control model and evaluate MRS‑CWC against six baseline methods and an ablation variant in a benchmark dataset with 100 different simulation terrains. Results show that MRS‑CWC achieves the highest navigation completion rate and ranks second in success rate, efficiency, and energy cost in the highly rugged terrain group, outperforming all baseline methods without relying on environmental modeling, path planning, or complex control. Even where MRS‑CWC ranks second, its performance is only slightly behind a more complex ablation variant with environmental modeling and path planning. Finally, we develop a physical prototype and validate its feasibility in a constructed rugged environment. For videos, simulation benchmarks, and code, please visit https://wyd0817.github.io/project‑mrs‑cwc/.

Abstract:
While cooperative perception can overcome the limitations of single‑vehicle systems, the practical implementation of vehicle‑to‑vehicle and vehicle‑to‑infrastructure systems is often impeded by significant economic barriers. Aerial‑ground cooperation (AGC), which pairs ground vehicles with drones, presents a more economically viable and rapidly deployable alternative. However, this emerging field has been held back by a critical lack of high‑quality public datasets and benchmarks. To bridge this gap, we present Griffin, a comprehensive AGC 3D perception dataset, featuring over 250 dynamic scenes (37k+ frames). It incorporates varied drone altitudes (20‑60m), diverse weather conditions, realistic drone dynamics via CARLA‑AirSim co‑simulation, and critical occlusion‑aware 3D annotations. Accompanying the dataset is a unified benchmarking framework for cooperative detection and tracking, with protocols to evaluate communication efficiency, altitude adaptability, and robustness to communication latency, data loss and localization noise. By experiments through different cooperative paradigms, we demonstrate the effectiveness and limitations of current methods and provide crucial insights for future research. The dataset and codes are available at https://github.com/wang‑jh18‑SVM/Griffin.

Abstract:
Current autonomous driving vehicles rely mainly on their individual sensors to understand surrounding scenes and plan for future trajectories, which can be unreliable when the sensors are malfunctioning or occluded. To address this problem, cooperative perception methods via vehicle‑to‑vehicle (V2V) communication have been proposed, but they have tended to focus on perception tasks like detection or tracking. How those approaches contribute to overall cooperative planning performance is still under‑explored. Inspired by recent progress using Large Language Models (LLMs) to build autonomous driving systems, we propose a novel problem setting that integrates a Multimodal LLM into cooperative autonomous driving, with the proposed Vehicle‑to‑Vehicle Question‑Answering (V2V‑QA) dataset and benchmark. We also propose our baseline method Vehicle‑to‑Vehicle Multimodal Large Language Model (V2V‑LLM), which uses an LLM to fuse perception information from multiple connected autonomous vehicles (CAVs) and answer various types of driving‑related questions: grounding, notable object identification, and planning. Experimental results show that our proposed V2V‑LLM can be a promising unified model architecture for performing various tasks in cooperative autonomous driving, and outperforms other baseline methods that use different fusion approaches. Our work also creates a new research direction that can improve the safety of future autonomous driving systems. The code and data will be released to the public to facilitate open‑source research in this field. Our project website: https://eddyhkchiu.github.io/v2vllm.github.io/ .

Abstract:
Cooperative perception enhances autonomous driving by leveraging Vehicle‑to‑Everything (V2X) communication for multi‑agent sensor fusion. However, most existing methods rely on single‑modal data sharing, limiting fusion performance, particularly in heterogeneous sensor settings involving both LiDAR and cameras across vehicles and roadside units (RSUs). To address this, we propose Radian Glue Attention (RG‑Attn), a lightweight and generalizable cross‑modal fusion module that unifies intra‑agent and inter‑agent fusion via transformation‑based coordinate alignment and a unified sampling/inversion strategy. RG‑Attn efficiently aligns features through a radian‑based attention constraint, operating column‑wise on geometrically consistent regions to reduce overhead and preserve spatial coherence, thereby enabling accurate and robust fusion. Building upon RG‑Attn, we propose three cooperative architectures. The first, Paint‑To‑Puzzle (PTP), prioritizes communication efficiency but assumes all agents have LiDAR, optionally paired with cameras. The second, Co‑Sketching‑Co‑Coloring (CoS‑CoCo), offers maximal flexibility, supporting any sensor setup (e.g., LiDAR‑only, camera‑only, or both) and enabling strong cross‑modal generalization for real‑world deployment. The third, Pyramid‑RG‑Attn Fusion (PRGAF), aims for peak detection accuracy with the highest computational overhead. Extensive evaluations on simulated and real‑world datasets show our framework delivers state‑of‑the‑art detection accuracy with high flexibility and efficiency. GitHub Link: https://github.com/LantaoLi/RG‑Attn

Abstract:
Vision‑Language‑Action (VLA) models have recently achieved impressive performance for end‑to‑end autonomous driving, yet existing approaches are primarily designed for an individual single autonomous driving agent with limited support for cooperative perception, reasoning, and planning. We present Cooperative Multi‑agent Unified Driving with Reasoning (CMU‑Drive), a closed‑loop end‑to‑end benchmark for evaluating cooperative autonomous driving with multiple connected autonomous vehicles (CAVs) operating in safety‑critical driving scenarios with background traffic participants. We further propose Vehicle‑to‑Vehicle Vision‑Language‑Action (V2V‑VLA), a cooperative VLA model that integrates cooperative driving into a single forward pass by jointly generating driving actions, future waypoints, language reasoning, and communication policies. Experiments on CMU‑Drive establish the first benchmark and baseline for cooperative VLA driving and provide a foundation for future research on multi‑agent, closed‑loop, end‑to‑end cooperative autonomous driving. Our code, benchmark, and model checkpoint will be publicly released to facilitate open‑source research.

Abstract:
Large Language Models (LLMs) are increasingly used as high‑level planners in embodied multi‑robot systems, enabling robots to interpret natural language instructions and coordinate executable actions. Yet, this growing reliance on LLM planners also raises security concerns. Prior work has focused mainly on individual robots, while communication risks in multi‑robot collaboration remain insufficiently understood. Existing multi‑robot studies are further limited to preliminary analysis under the Decentralized Multi‑agent System (DMAS) architecture, so it remains unclear whether these risks persist across other common communication architectures and how attacker access settings shape their propagation. To fill this gap, we formulate two communication attacks corresponding to distinct attacker access settings: the External Entry Point Attack and the Privileged In‑System Attack. We evaluate both attacks across DMAS, HMAS‑1, and HMAS‑2 using three LLMs and five embodied multi‑robot tasks. Results show that unsafe information can turn into unsafe actions across all three architectures: DMAS reaches a 96.7% entry endorsement rate and a 100% post endorsement activation rate, HMAS‑1 reaches a 97.8% unsafe action success rate, and HMAS‑2 triggers 88.3% of task defined unsafe action slots. To mitigate risks from trusted information flow, we introduce the Claim Provenance and Verification (CPV) Gate, which verifies communicated claims before downstream reuse and reduces the violation rate from 70.0% to 36.6%.

Abstract:
Accurate localization remains a key challenge in swarm robotics, particularly for self‑reconfigurable systems that must identify relative positions to form diverse structures. Most existing approaches rely on external tracking infrastructure or high‑cost sensors, which limit scalability and deployment in unstructured environments. In this paper, we propose a novel contact‑driven localization method for modular robots that leverages only local communication through binary contact information (whether two robots are physically connected or not). To exploit these contact cues, we introduce a virtual‑force framework in which robots iteratively refine their poses attracting toward dock‑connected neighbors and repelling from non‑connected ones. The method requires no external infrastructure and relies only on minimal onboard sensing. Simulations show effective localization during the assembly of towers and cantilevers, enabling accurate, scalable, free‑form self‑assembly.

Abstract:
Cooperative perception (CP) enables connected autonomous vehicles (CAVs) to share complementary observations for safer navigation, but practical deployment is limited by bandwidth constraints, unreliable links, and redundant information exchange. Existing CP methods often assume predefined participants and merely focus on collective perception. Likewise, recent LLM‑based cooperative driving frameworks facilitate multi‑vehicle reasoning but do not regulate participation criteria to select more beneficial vehicles. To bridge this gap, we propose an LLM‑assisted coalition formation framework that selects the most informative helper vehicles before LLM reasoning. The approach jointly optimizes perceptual diversity using a determinantal point process (DPP) over multimodal vehicle embeddings and communication‑aware reliability. This leads to a joint coalition selection and power allocation problem, which we solve efficiently via a relaxed convex reformulation and an ADMM‑based optimization strategy that decouples diversity‑aware selection from network‑aware resource allocation. The selected coalition is then summarized and provided with an LLM reasoning module for efficient and less redundant multi‑vehicle decision support. Experimental results show that our approach outperforms other baselines in overall coalition value, while maintaining high diversity and improved networking efficiency. The framework achieves a better balance between task performance and safety across OPV2V and V2V4Real datasets, demonstrating its effectiveness for cooperative autonomous driving with communication constraints.

Abstract:
Cooperative task scheduling in communication‑constrained distributed multi‑agent systems is challenging because each agent must make decisions from partial and dynamic observations while satisfying complex practical constraints. Existing heuristics rely on handcrafted bidding rules and repeated consensus, whereas many learning‑based methods assume global observations and lack explicit communication‑based coordination. To address these limitations, this paper proposes a neural scheduling framework for distributed multi‑robot task allocation (MRTA), consisting of a multi‑decoder graph attention model (MDGAM) policy model and a critic‑free group relative multi‑agent policy gradient (GRMAPG) training algorithm. MDGAM uses an extended graph attention mechanism to jointly update node and edge features, and employs multiple decoders to generate task‑selection decisions and communication messages. GRMAPG constructs group‑relative advantages from equivalent task‑planning instances to replace the critic network used in conventional MARL algorithms, thereby reducing training difficulty and improving convergence performance. Experiments under different problem scales and communication ranges show that the proposed method improves task‑completion performance over existing heuristic and learning‑based methods, while ablation, complexity, and generalization tests further validate the proposed innovations.

Abstract:
Motion planning in dynamic environments is a fundamental problem in robotics, aiming to generate safe and efficient paths, trajectories, or control actions in the presence of moving obstacles, uncertain predictions, and multi‑agent interactions. It has broad applications in autonomous driving, service robotics, warehouse logistics, human‑robot collaboration, crowd navigation, and multi‑robot systems. This survey reviews representative works published primarily between 2015 and 2025, with a particular focus on how recent learning‑based advances extend, complement, or interact with classical planning foundations. We first revisit classical planning methods as algorithmic foundations and reference frameworks for learning‑based extensions. We then propose a role‑of‑learning taxonomy that categorizes existing methods according to how learning participates in the planning pipeline, including direct policy learning, learning‑augmented classical planning, hybrid planning, and training enhancement methods. For each category, we summarize the main problem settings, representative algorithms, key ideas, integration mechanisms, strengths, and limitations. We further analyze how observation representations, prediction uncertainty, interaction modeling, planner integration, safety constraints, and training strategies shape learning‑based motion planning in dynamic environments. Finally, we discuss open challenges and future directions, including sim‑to‑real gap, safe and certifiable planning, dense crowd navigation, perception‑planning coupling, and embodied AI.

Abstract:
Multi‑view Multi‑object Association and Tracking (MvMoAT) associates objects across camera views and tracks them over time, supporting identity persistence and forensic trajectory reconstruction in multi‑platform cooperative perception. Unlike conventional multiple object tracking, MvMoAT faces frequent viewpoint shifts that distort appearance and undermine cross‑view association and temporal tracking. We propose FUSION, a viewpoint‑robust Feature Unification framework for multi‑view aSsociation and IdentificatiON. Its Multi‑cue Adaptive Combination (MAC) module adaptively integrates viewpoint‑invariant cues with appearance features to improve cross‑view association, while Online Multi‑view Feature Synchronization (OMFS) aggregates pedestrian features across historical and cross‑view frames for temporally consistent tracking. We also introduce RealMvMoAT, a large‑scale benchmark featuring substantial inter‑ and intra‑camera viewpoint variation. It contains 504.9K frames from 7 cameras (5 UAV and 2 ground views) across 10 scenes, with over 7.3M identity‑labeled bounding boxes. All cameras exhibit random and substantial motion. To the best of our knowledge, RealMvMoAT is the largest MvMoAT dataset to date. Its scale, viewpoint diversity, complex platform motion, and realistic trajectories provide a comprehensive resource for future research. Experiments on RealMvMoAT and six public benchmarks show that FUSION achieves state‑of‑the‑art performance.

Abstract:
Trajectory optimization for multi‑robot systems remains a critical challenge, particularly when navigating highly non‑convex, non‑linear, and non‑differentiable environments. While Model‑Based Diffusion (MBD) has recently emerged as a promising sampling‑based optimization paradigm for single‑robot trajectory generation, extending it to multi‑robot systems results in a centralized, high‑dimensional inference problem that (i) suffers from poor sample efficiency due to the curse of dimensionality and (ii) requires global access to all robots' dynamics, constraints, and objectives. To address this, we propose Distributed Model‑Based Diffusion (DMBD), a distributed server‑robot framework that decomposes the reverse diffusion process into local conditional reverse diffusion processes. This decomposition enables each robot to iteratively perform denoising independently within its own control subspace while conditioning on the current trajectory estimates of the other robots that are aggregated and broadcast by the server. Extensive simulations in goal swapping, multi‑floor coverage, parking, and rush‑hour scenarios demonstrate that DMBD achieves strong scalability, solving many challenging coordination tasks in sub‑seconds and significantly outperforming existing baselines.

Abstract:
Cooperative perception enables autonomous vehicles (AVs) to improve situational awareness by aggregating detection outputs from multiple agents and sensing platforms, often via a shared fusion service in multi‑vendor deployments. However, sharing such outputs at inference time exposes proprietary model behavior and sensitive environmental information, creating significant privacy and security concerns. In this paper, we present Sarus, a privacy‑preserving framework for multi‑vendor perception fusion via homomorphic encryption (HE), enabling aggregation without revealing individual vendor outputs. Each vendor encodes detections as compact Gaussian moment vectors over a shared spatial lattice and transmits encrypted payloads to a fusion server, which aggregates them directly in the encrypted domain. The fused result is then decrypted and reconstructed into final detections through class‑wise bin merging. We analyze the computational complexity, showing linear scaling for vendor payload construction and O(BV) server‑side fusion with the number of occupied bins B and vendors V, while postprocessing scales as O(B + \sum_c\in \mathcalC B_c^2), where \mathcalC denotes the set of object classes and B_c is the number of occupied bins for class c. Experiments demonstrate linear scaling in practice with only a bounded constant‑factor overhead from HE, with decryption dominating postprocessing cost. Experiments on the KITTI dataset using camera (YOLOv8) and LiDAR (PointPillars, PV‑RCNN) detectors show that Sarus improves scene‑level coverage by effectively aggregating complementary detections, particularly in distance‑dependent regimes where individual modalities degrade. These results indicate that privacy‑preserving multi‑vendor perception fusion is feasible for real‑time deployment when statistical compression and spatial sparsity are jointly exploited.

Abstract:
The simultaneous arrival of multiple mobile robots at a target point is crucial for cooperation tasks such as cooperative encirclement, disaster relief, and environmental monitoring. Although the simultaneous arrival problem itself is already complex, the problem becomes more challenging when there are constraints on the robot trajectory curvatures and the speeds are required to be constant (possibly different for different robots), and the control law for robots needs to be distributed. These constraints are typical for a multi‑robot system consisting of, e.g., fixed‑wing UAVs. To address this challenge, this paper proposes a distributed switching control method based on the maximum consensus protocol. By exploiting the geometric properties of Dubins paths along with optimization principles, a virtual time variable is introduced, and a hybrid control law that combines optimal control with saturated proportional control is designed. Under the proposed control law, each robot is driven to approach the maximum virtual time among its neighbors, thereby achieving simultaneous arrival under some mild conditions. Furthermore, we prove that in certain cases the proposed method attains a theoretically optimal arrival time. The approach is scalable and real‑time, with low communication overhead. Its effectiveness and robustness are validated through extensive simulations and experiments.

Abstract:
Relative pose estimation is a fundamental capability for collaborative perception and coordination in multi‑robot systems. However, robots encountering each other in real‑world environments often operate in short interaction windows and must operate under limited communication bandwidth with intermittent or missing visual overlap caused by occlusions or limited fields of view. Existing approaches typically rely on global reference frames, assume sustained view overlap, or incur prohibitive communication costs, thereby limiting their applicability to ephemeral collaborative perception. To address these challenges, we introduce communication‑efficient relative pose estimation (CERPE), a system‑level framework that coordinates vision foundation models to jointly estimate ego‑motion and inter‑robot relative pose. CERPE reduces unnecessary raw‑observation exchange by using continuously shared fixed‑size descriptors to gate event‑triggered raw‑image requests independently of pose estimation. Non‑overlapping encounters are handled by propagating inter‑robot relative poses through metrically scaled ego‑motion, thus maintaining relative pose estimates even in the absence of visual overlap. Experiments in simulation and real‑world robots show that CERPE improves 6‑DoF relative pose estimation over selected baselines in ephemeral collaborative perception.

Abstract:
European safety regulation now permits a large share of automated‑driving homologation evidence to be produced virtually, provided a validated physical‑virtual facility generates it. We present a deployed hybrid Vehicle‑in‑the‑Loop (ViL) platform that couples a real instrumented vehicle with a CARLA‑based digital twin (DT) through a V2X message pipeline, and we report its first integrated operation on a public‑road‑representative test track. A real vehicle streams ETSI‑compliant CAM/CPM messages into the DT, where a GPU‑accelerated Cooperative Perception (CP) module fuses them into a probabilistic occupancy grid during scenario runtime. We demonstrate the platform on a multi‑vehicle double T‑intersection scenario, characterise the CP workload across nominal, rain and night conditions and five localization‑noise levels, and discuss the platform's current architectural limits and the engineering targets they define. The results show that CP substantially widens field‑of‑view (FoV) coverage and improves occupied‑cell recall, and that beyond a moderate localization‑noise threshold, positioning uncertainty, and not weather, becomes the dominant error source. We outline the platform's trajectory toward a Mediterranean operational design domain (ODD) testing service.

Abstract:
Efficient task allocation for large‑scale Heterogeneous Multi‑Robot Systems (HMRS) is critical, yet dealing with complex temporal logic tasks in partially known environment (PKE) remains a computational bottleneck. Existing approaches often struggle to balance exploring uncertain regions and exploiting known resources, while also suffering from exponential computational complexity. To address these issues, this paper presents a robust planning framework that simultaneously handles high‑level logical constraints and environmental uncertainty without sacrificing scalability. We formulate the problem as a min‑max regret optimization, proposing a Region‑Binding Atomic Proposition (RbAP) to capture resource uncertainty within the automaton structure. To solve this, we propose the Extended Planning Decision Tree (E‑PDT) equipped with a novel Regret‑based Branch‑and‑Bound (BnB) strategy. Unlike traditional methods that rely on prior probabilities or worst‑case analysis, our approach dynamically prunes suboptimal policies, effectively balancing the need for information gathering (exploration) and task completion (exploitation). Theoretical analysis confirms the feasibility and completeness of our approach. Extensive numerical and physical experiments demonstrate that the proposed framework achieves near‑linear scalability with respect to the number of robots and types, significantly outperforming MILP‑based baselines in both solution quality and computational efficiency.

Abstract:
Coverage path planning (CPP) is a fundamental problem in robot motion planning, whose aim is to produce robot trajectories that provide complete coverage of target workspaces while minimizing task‑specific objectives such as path length, overlap, number of turns, and energy consumption. CPP has widespread applications in cleaning, inspection, mapping, agriculture, manufacturing, surveillance, demining, and environmental monitoring. Although classical CPP has been extensively studied, recent advances have extended CPP beyond single‑robot settings to multi‑robot systems, complex 3D environments, constrained platforms, learning‑based coverage planning, and visual coverage tasks. This paper presents a comprehensive survey of 125 representative works published primarily between 2015 and 2026, while presenting the evolution of recent developments in light of the classical CPP methods published before 2015. The CPP methods are organized into six main categories: single‑robot CPP, multi‑robot CPP, 3D CPP, constrained CPP, learning‑based CPP, and visual CPP. For each category, the review summarizes the main planning formulations, representative algorithms, strengths, and limitations. In addition, the review analyzes how environmental knowledge, workspace geometry, robot constraints, sensing objectives, and coordination requirements shape the CPP problem. The survey further discusses open challenges in scalable online planning, multi‑robot coordination, 3D and visual coverage, unified platform‑constrained and resource‑aware coverage, and learning‑enhanced coverage. Thus, the survey provides a structured overview of recent CPP developments and future research directions.

Abstract:
Communication graph rigidity is a fundamental requirement in many multi robot formation control approaches. However, ensuring and maintaining a rigid communication topology becomes challenging in practice due to limited sensing ranges and dynamic operating conditions. This paper provides a method for achieving an inter robot collision free, rigid time varying communication graph, where communication links are established or broken according to limited sensing ranges, without assuming an initial rigid graph. In addition, the proposed approach guarantees the realization of a rigid graph for heterogeneous nonlinear multi robot systems. A computationally lean, distributed quadratic optimization‑based controller is developed for a leader follower architecture, acquiring rigidity based on hierarchical second‑order consensus among robots. Follower agents do not require global absolute positions of any agent, including their own. The proposed method is validated through both simulations and hardware experiments in a motion‑capture environment, demonstrating reliable performance under the limited sensing capabilities of individual robots.

Abstract:
Collaborative decision‑making is a fundamental capability in multi‑robot systems, such as connected autonomous vehicles. However, perceptual noise and adversarial attacks in collaborators can severely affect decision reliability. Overall, existing methods typically rely on retraining with attack‑specific defenses or on restrictive perturbation assumptions to improve resilience, which limits their practicality. In this paper, we propose a novel Resilient Collaborative Decision‑Making (RCDM) framework that consists of an attention‑based encoder for extracting individual robot perceptual embeddings and an attention‑based decoder for fusing collaborator perceptions and making decisions. To improve resilience to corrupted observations, we design a novel plug‑and‑play reweighting module that down‑weights the influence of corrupted inputs by analyzing the consistency of neighborhood points relative to the local structure and assigning smaller weights to points that deviate strongly from the local median. This module can be seamlessly integrated into attention‑based collaborative decision‑making without requiring additional training. We evaluate our method in high‑fidelity simulations, considering perceptual noise and five types of attacks across diverse accident‑prone scenarios. Experimental results demonstrate that our approach consistently outperforms existing methods by up to 26% and achieves state‑of‑the‑art resilient performance.

Abstract:
Autonomous exploration in unknown environments is typically driven by informative frontiers, viewpoints, or trajectories, while local safety controllers avoid obstacles represented in the current map. Under finite sensing range and limited field of view, this separation can be unsafe: an exploration stack may plan optimistically through unobserved space and steer the sensor toward information gain rather than along the direction of motion, causing hidden obstacles to be detected too late for bounded‑actuation avoidance. This paper presents SEAMLiS (Safe Exploration for Autonomous Multi‑Robot Systems Under Limited Sensing), a modular execution‑layer safety framework for decentralized multi‑robot exploration. SEAMLiS preserves the upstream exploration stack, including the goal allocator and local planner, and enforces safety at the execution layer through perception‑aware attitude and positional filters. A gatekeeper‑based attitude filter switches between a visibility‑promoting yaw policy and a velocity‑tracking backup policy to preserve visibility of the critical known‑free/unknown boundary with sufficient braking margin. A Control Barrier Function (CBF)‑based positional filter then avoids known obstacles, newly detected obstacles, and other robots. We provide sufficient collision‑avoidance conditions and validate the framework in randomized simulation, Isaac Sim, and Crazyflie hardware experiments. Results show collision‑free exploration across tested single‑ and multi‑robot settings while retaining much of the efficiency of visibility‑promoting yaw control.

Abstract:
Multi‑robot path planning in human‑shared environments requires a delicate balance between robust inter‑robot coordination and socially aware behavior. While diffusion models excel at generating predictable, human‑like paths, existing generative planners are often restricted to paths of fixed duration and high computational latency, limiting their adaptability to varying goal distances and hindering real‑time deployment. We present Multi‑Robot Rolling Diffusion (MRRD), a novel framework that enables real‑time, long‑horizon navigation for large robot teams through dense crowds. MRRD combines a rolling‑horizon scheme to accommodate the limited prediction horizon of human motion, parallelized diffusion inference for scalable generation of human‑like paths, and a conflict‑based‑search mechanism for resolving inter‑robot collisions. It further incorporates urgency‑based temporal conditioning to generate paths with varying speeds and employs differentiated guidance terms to maximize both social awareness around humans and efficient coordination between robots. Experimental results in crowded environments demonstrate that MRRD successfully scales to 15 robots in real‑time, significantly outperforming existing baselines in both safety and mission success rates.

Abstract:
This paper introduces a digital‑twin (DT)‑assisted cooperative perception framework designed to improve perception accuracy under end‑to‑end (E2E) latency constraints and to balance perception accuracy and E2E latency under communication resource constraints in autonomous vehicles. We formulate an optimization problem that maximizes perception accuracy subject to latency and communication limitations, and solve it using a newly proposed coarse‑to‑fine search (CTFS) algorithm. Simulation results show that the proposed CTFS algorithm achieves 96.6% perception accuracy, close to exhaustive search, under latency constraints while reducing computational complexity by approximately 85.78%. The DT‑assisted framework further achieves a 50% reduction in the non‑DT communication cost through estimated, time‑synchronized state updates.

Abstract:
Multi‑robot localization that is accurate and consistent is imperative for downstream tasks such as planning and control. Centralized filtering approaches optimally fuse all available sensor measurements of the team. However, a centralized solution is rarely implementable due to hardware, communication, and computational constraints. Distributed approaches deploy a filter on each robot to estimate their own state and neighbours' states using inter‑robot communication. This paper proposes a consistent, communication‑efficient, and consensus‑based distributed filtering framework that shares both preintegrated odometry and relevant shared states among communicating robots. The proposed method is validated in simulated and experimental scenarios, showing near centralized performance in accuracy, and especially in consistency, compared to the current state‑of‑the‑art decentralized approach.

Abstract:
Automated vehicles rely on onboard sensors to perceive their surroundings and navigate autonomously. However, sensor performance may degrade under adverse weather conditions or when line‑of‑sight is obstructed. Cooperative perception (or collective perception) is expected to mitigate these limitations by enabling Connected and Automated Vehicles (CAVs) to share sensor data and collaboratively enhance situational awareness. Several studies have analyzed the potential of cooperative perception, yet the fusion of V2X data with information from onboard sensors has received limited focus. V2X data may contain errors that affect the quality of the fused data, and hence the effectiveness of cooperative perception. This study analyzes the impact of sensing measurement errors, V2X packet losses, and GNSS inaccuracies on the effectiveness of cooperative perception. The results highlight the potential of cooperative perception to enhance perception levels and range compared to using onboard sensors alone. However, they also identify key challenges related to the generation of ghost vehicles during the fusion process, which must be addressed to prevent V2X data from introducing additional errors when fused with onboard sensor data.

Abstract:
Multi‑robot systems provide the parallelism and redundancy necessary for long‑horizon tasks, while Large Language Models (LLMs) offer the reasoning capabilities to decompose these objectives into actionable plans. However, effectively grounding this high‑level reasoning in physical multi‑robot execution remains an open challenge. Existing LLM‑based approaches fall mainly into two categories: Single‑robot methods achieve robust contact‑rich manipulation but lack the coordination mechanisms required for tasks spanning multiple workspaces. Current multi‑robot frameworks focus on high‑level planning, often treating manipulation as an idealized primitive that fails to account for real‑world execution uncertainties. To address this, we propose a hierarchical closed‑loop agentic LLM‑based framework to ensure robust multi‑robot manipulation. Our system consists of three specialized agents: the Planning Agent decomposes instructions into allocated sub‑tasks, the Manipulation Agent for each robot executes actions via adaptive tool use, and the Verification Agent closes the loop by monitoring physical outcomes and feeding back semantic corrections. Extensive real‑world experiments demonstrate that our framework achieves superior success rates, ensures robust adaptability ranging from single to cross workspace manipulation, and offers a generalizable approach for diverse manipulation tasks.

Abstract:
Connected Automated Vehicles (CAVs) utilize their onboard sensors to perceive the environment. The perception range and accuracy can be affected by adverse weather or non‑line‑of‑sight conditions. Cooperative perception or sensor sharing can overcome these limitations by enabling CAVs to exchange sensor data, thus collectively enhancing their perception capabilities. Previous studies have shown the potential of cooperative perception, but limited attention has been given to the fusion of V2X data received through cooperative perception messages with onboard sensor information. The fusion process can be influenced by the quantity and quality of the V2X data. An increased volume of V2X data can reduce uncertainty in the perceived environment; however, when the data is noisy, it may compromise the accuracy of the fusion results. This study investigates the fusion of onboard sensor and V2X data in cooperative perception, and demonstrates that while perception can significantly improve as the V2X penetration rate increases, it can introduce a significant number of false positives if V2X data is not highly accurate. False positives result in the detection of ghost objects that do not actually exist. These ghost objects can, in turn, compromise safety and driving efficiency. Our analysis found that false positives or ghost objects can appear even with accurate V2X data. These findings highlight the challenges in cooperative perception and the importance of developing robust data fusion methods to enhance the reliability of cooperative perception. This is particularly relevant in light of ongoing standardization efforts, such as ETSI TS 103 324 on collective perception.

Abstract:
Existing classical control methods commonly require precise models and struggle to cope with model uncertainties and external disturbances, while end‑to‑end reinforcement learning (RL) approaches suffer from low sample efficiency and poor convergence. To overcome these challenges, this paper proposes a hierarchical hybrid physics‑informed deep reinforcement learning (HHy‑PIDRL) framework, aiming to realize high‑precision, highly responsive formation control for heterogeneous multi‑robot systems (HMRSs). The proposed framework contains two layers. Specifically, first, the upper layer designs an autonomous navigation policy network for Ackermann‑steering leader based on the Soft Actor‑Critic (SAC) deep reinforcement learning (DRL) algorithm. Second, the lower module integrates a high‑fidelity physical feed‑forward controller, a classical proportional‑derivative (PD) controller, and an adaptive DRL residual controller to propose an effective hybrid model and DRL (HM‑DRL)‑based formation control policy network. Third, a unique hierarchical reward function is designed for training Omnidirectional followers, which effectively guides agents toward a refined, stable control policy. Experimental results demonstrate that, the success rate of both the upper‑layer autonomous navigation policy network and the HM‑DRL based formation control policy networks reach 100%. Meanwhile, ablation experiments are conducted to verify the validity and credibility of the proposed method.

Abstract:
Distributed trajectory estimation arises in many applications across robotics, but existing implementations typically do not consider asynchrony in agents' communications and computations. Therefore, we propose an asynchronous block coordinate descent algorithm for distributed trajectory estimation. We consider a team of agents that observes a team of robots and estimates their states over a sliding window. The agents solve an approximation of the maximum a posteriori estimation problem, which we derive. We show this approximation introduces negligible errors and eliminates up to 96.9% of communications among agents. Next, we prove that agents' iterates converge exponentially fast to the optimal estimate of the robots' states. Simulations show that this approach has up to 64% less error than a comparable state‑of‑the‑art algorithm. Experiments on mobile robots show the robustness of this approach to delays whose lengths span three orders of magnitude.

Abstract:
Agentic artificial intelligence is increasingly used to decompose industrial tasks, propose robot actions, and adapt execution plans in dynamic cyber‑physical environments. However, autonomous proposal generation alone does not guarantee that multi‑robot industrial systems preserve task dependencies, resource ownership, safety holds, or repair boundaries during long‑horizon execution. This paper introduces a verification‑gated agentic mission‑state governance framework for intelligent industrial multi‑robot systems. The framework maintains two synchronized state objects: an evolving task forest for persistent hierarchy, delayed grounding, and repairable substructures; and a governed blackboard for online execution state, robot traces, resource locks, world beliefs, proposals, verification records, and scene‑temporary constraints. From each forest‑‑blackboard snapshot, a derived execution coupling topology exposes cross‑branch dependencies for proposal verification, parallel‑commit eligibility, and bounded repair. Candidate assignments, repairs, deferrals, and constraint updates may be generated by heuristic, optimization, or agentic reasoning modules, but they can update the committed mission state only after deterministic verification and atomic commit. We evaluate the framework in an indoor factory multi‑robot scenario, 30‑seed remote‑construction stress benchmarks, structural ablations, and scalability probes. The results show improved verified and safety‑audited mission‑state progress with fewer invalid commitments, lock conflicts, duplicate assignments, abandoned nodes, and disruptive repairs under modeled mission predicates. The study positions agentic AI as a proposal‑generating layer governed by inspectable mission‑state verification rather than as an unchecked execution authority.

Abstract:
Cellular vehicle‑to‑everything (C‑V2X) enables cooperative perception, prediction, and planning beyond the field of view of individual agents. However, existing datasets often overlook the complexities of real‑world deployment, such as limited communication bandwidth and its dynamics, heterogeneous sensing modalities, and scalability beyond a single cooperative partner. In this paper, we introduce CooperScene, a high‑fidelity cooperative autonomy dataset with real‑world C‑V2X communication characterization. The dataset is organized into diverse scenes, including intersections, highway ramps, and parking lots. These scenes involve three connected and autonomous vehicles (CAVs) and one infrastructure roadside unit (RSU), all equipped with multi‑modal sensors and commercial off‑the‑shelf C‑V2X communication radios. All scenes are annotated with globally consistent 3D labels at 10 Hz, totaling 344K objects across 59K frames, underpinned by tight sensor‑ and agent‑synchronization, centimeter‑level localization and spatial alignment, precise cross‑modality calibration, and 3GPP‑standard‑compliant C‑V2X communication. CooperScene establishes a rigorous benchmark for evaluating multi‑agent scaling and actual performance in real‑world deployable settings. Project website for data and benchmark: https://cisl.ucr.edu/CooperScene

Abstract:
Multi‑robot systems must simultaneously optimize competing objectives while maintaining coordinated behavior. Existing multi‑agent reinforcement learning approaches often rely on fixed or centralized coordination, which limits adaptability and violates distributed constraints. This work introduces the Coordination‑Informed Multi‑Objective Reinforcement Learning (CIMORL) framework, integrating a distributed weight prediction mechanism, a privileged expert training strategy, and theoretical guarantees for Pareto‑optimal solutions. We present the base CIMORL method alongside two sampling‑based variants, CIMORL‑TS (Tree Search) and CIMORL‑MPPI (MPPI), which leverage privileged global information during training to enable fully decentralized deployment. Experimental validation in cooperative and adversarial scenarios demonstrates a 21.2% hypervolume improvement and superior policy stability compared to state‑of‑the‑art baselines. Real‑world experiments with Crazyflie drones further validate the framework's robustness in resource allocation and multi‑attacker multi‑defend scenarios under partial observability.

Abstract:
Cooperative localization using range‑based measurements is critical for multi‑robot systems operating in GPS‑denied and unstructured environments. However, traditional cooperative approaches require sharing explicit spatial coordinates across the network, presenting a severe security vulnerability in privacy‑sensitive missions. While recent literature has explored privacy‑preserving alternatives, these methods typically rely on accuracy‑degrading noise injection or computationally prohibitive cryptographic protocols. To overcome these limitations, we propose a novel, natively privacy‑preserving Decentralized Cooperative Localization (DCL) framework based on convex optimization. Discarding probabilistic noise models, we assume strictly bounded measurement noise and formulate the localization problem via Semi‑Definite Programming (SDP) to compute a Maximum‑Volume Inscribed Ellipsoid (MVE). Our approach introduces novel intersection‑plane constraints derived from landmark measurements to significantly tighten individual spatial bounds. To incorporate inter‑robot range measurements securely, we uniquely decompose coupling constraints into localized Linear Matrix Inequalities (LMIs). Agents achieve fleet‑wide spatial consensus by iteratively exchanging only abstract dual variables, completely avoiding the transmission of explicit primal position estimates. Extensive 3D Monte Carlo simulations demonstrate that our DCL framework outperforms existing SDP‑based localization method in accuracy, while guaranteeing operational privacy and maintaining highly scalable, parallelizable computation.

Abstract:
Embodied AI is increasingly becoming agentic, shifting robots from perception‑‑control pipelines towards closed‑loop systems that can retrieve context, deliberate during execution, monitor feedback, and refine future behavior. In parallel, robotics research has also moved from single‑robot autonomy towards multi‑robot systems, driven by the need for wider sensing, distributed action, heterogeneous capabilities, and fault tolerance. As AI agents move from single‑agent use towards multi‑agent collaboration, robotics faces a parallel challenge: robot teams must move beyond sharing maps, task assignments, and datasets towards sharing the state produced by embodied agent loops. This article explores Embodied Collective Intelligence (ECI), a future multi‑robot paradigm in which a robot team accumulates and uses world context, task progress, and skill experience as shared resources. Specifically, we first review how embodied AI is becoming agentic and how multi‑robot cooperation has evolved. We then present Embodied Collective Intelligence through Co‑Perception, Co‑Action, and Co‑Evolution. Finally, we use an illustrative navigation study to examine one concrete component of the concept: shared world‑memory inheritance. The study shows that a newly added robot can benefit from merged team memory, but it is not intended as a full evaluation of the ECI framework. Taken together, the review and conceptual framework motivate Embodied Collective Intelligence as a direction for embodied multi‑agent intelligence, while the case study grounds one measurable part of the concept.

Abstract:
In stealth‑constrained swarm robotics, visual communication provides a critical alternative to active radio transmissions, which might be jammed. This research investigates motion‑based communication for non‑active information exchange, utilizing modular, dynamically feasible planar trajectories as visual cues. On the receiver drone end, a pose estimator tracks the transmitting drone's pose, feeding it into our custom 3DTrajDecoder. The decoder is designed to classify and segment the spatiotemporal sequence while simultaneously regressing its size and normal vector. To robustly train the decoder on both communicative and non‑communicative trajectories, we developed a configurable online procedural generation pipeline. We validate our system through real‑world testing and simulation to define its operating domain, supported by an extensive ablation study detailing our architectural choices and system limitations.

Abstract:
Pose graph optimization (PGO) is a key back‑end component for state estimation in networked multi‑robot simultaneous localization and mapping (SLAM). In object‑based multi‑robot SLAM, the problem becomes more tightly coupled because robots must jointly estimate both their trajectories and the poses of persistent objects observed by multiple agents. Existing decentralized solutions often assume that the communication graph closely matches the physical interaction topology, which is restrictive in realistic deployments where communication is sparse, intermittent, or time‑varying. This paper presents a fully decentralized Riemannian optimization framework for object‑based multi‑robot PGO that decouples the coupled estimation problem via a consensus mechanism, enabling flexible communication topologies. To improve convergence under limited communication budgets, we further develop a distributed approximate‑Newton scheme that exploits local second‑order information while operating directly on the SE(d) manifold to preserve geometric consistency, and we establish the convergence to Riemannian first‑order stationary points and provide a local condition‑number analysis explaining the benefit of approximate second‑order information over first‑order Riemannian descent. The resulting method reduces iteration count and communication overhead without sacrificing estimation accuracy. Extensive evaluations on public benchmarks, large‑scale simulations, and real‑world multi‑robot experiments demonstrate improved accuracy, runtime efficiency, scalability across network topologies, and robustness to communication failures.

Abstract:
This paper presents a scalable framework for multi‑robot task allocation in complex environments where estimating task execution costs is computationally expensive. While combinatorial auction‑based approaches offer reliable solutions, the exponential complexity of bundle generation typically renders them intractable for real‑time reactive applications, particularly when accurate path planning is required for cost validation. We address this through a distributed, two‑stage multi‑fidelity bundle generation approach. Agents utilize a local search tree guided by a low‑fidelity heuristic (such as euclidean distance) to rapidly explore the bundle space, applying high‑fidelity path planning only to the most promising candidates in a best‑first manner. These refined bids are then submitted to a central coordinator that solves a set packing problem to ensure global feasibility and maximize the overall utility. Simulation results in multiple environments demonstrate that the framework is able to improve the performance of reactive auction‑based task allocation. Overall, the presented framework is shown to enable reactive task allocation with dynamic bundle sizes in multiple settings without exposing the agents' state and internal cost estimation models.

Abstract:
Task specialization can improve the efficiency of multi‑robot systems (MRSs). Previous works have investigated the emergence of task‑specialist robot controllers through evolutionary optimization and have argued that task specialization is more likely to evolve when subtask behaviors are readily available as building blocks. However, the available evaluation budget must be distributed across all subtasks, whereas a single generalist behavior can exploit the entire budget for its own optimization. We present a cost‑benefit analysis of evolving task‑specialist versus generalist behaviors in a foraging scenario here. In a physics‑based robotics simulator, we study the total evaluation budget required to evolve task‑specialist behaviors that outperform generalist behaviors across MRS sizes. We show that with increasing MRS size, a lower total evaluation budget is sufficient to evolve specialists that outperform generalists.

Abstract:
Large language models (LLMs) provide robots with richer task understanding and adaptability, making them promising for coordinating heterogeneous multi‑robot systems in long‑horizon tasks. Despite this potential, several challenges remain underexplored: (1) Centralized LLM schedulers scale poorly as team size and environmental complexity increase. A single model must process excessive contextual information, and long‑context approximation may degrade reasoning quality; (2) Existing task formulations insufficiently consider dynamic settings, while robust adaptation to evolving task conditions is essential for real‑world deployment; (3) Domain‑specific data scarcity limits specialized robotic reasoning, making proprietary general‑purpose models inefficient for expert tasks. To address these limitations, we propose DynaHMRC, a decentralized framework in which each robot acts as a role‑aware LLM agent. This design mitigates the single‑model context bottleneck and supports flexible collaboration across heterogeneous team configurations. DynaHMRC organizes collaboration as a four‑stage closed‑loop process: self‑description, task allocation with leadership bidding, leader election, and reflective execution, supported by executable robot interfaces. We further develop a benchmark covering three task families, four dynamic variations, and six team configurations to systematically study dynamic task modeling. In addition, we conduct an empirical analysis to guide the construction of domain‑specific expert datasets and fine‑tune pretrained LLMs to improve specialized competence. Experiments show that DynaHMRC achieves higher success rates than strong baselines with fewer action and communication steps, while demonstrating promising scalability trends as team size grows within the evaluated settings.

Abstract:
Deep learning models are increasingly central to autonomous vehicle (AV) pipelines, yet their integration has traditionally followed a monolithic design where perception, planning, and control execute on a single onboard computer. This design overlooks the emerging paradigm of cooperative autonomy, where vehicles interact with roadside units (RSUs), edge servers, and cloud‑hosted intelligence through vehicle‑to‑everything (V2X) connectivity. Cooperative perception and control improve safety and efficiency, but also introduce systems‑level challenges: network latency, compute heterogeneity, and multi‑tenant contention, all critically affect real‑time decision‑making. These challenges are further amplified by the increasing reliance on large foundation models, whose scale necessitates cloud deployment. We present CADET (Cooperative Autonomy through Distributed Experimentation Toolkit), a modular platform for systematic and reproducible evaluation of distributed cooperative autonomy systems under realistic deployment conditions. CADET decouples the AV stack into composable modules that can be flexibly deployed across vehicles, infrastructure, and edge/cloud tiers. The framework integrates state‑of‑the‑art models, incorporates trace‑driven network and workload emulation, and provides synchronized model‑, system‑, and task‑level instrumentation. Through V2V and V2I experiments, we show that distributed deployment choices fundamentally shape safety, with V2V intent packets outperforming cloud‑based perception and RSU‑assisted perception sustaining safety until overloaded by concurrent requests. Although designed for AV pipelines, CADET also supports dataset‑driven experimentation, enabling systems and ML researchers to benchmark distributed inference workloads independently of full vehicle simulation. CADET is open source, with code and demo available at https://nesl.github.io/cadet‑web.

Abstract:
This letter proposes a novel distributed bearing‑based pose estimator for time‑varying multi‑robot systems. The method uses angles computed from body‑frame bearings to estimate the robots' positions in \mathbbR^3 without knowledge of their orientations. The orientations in \mathrmSO(3) are recovered from the estimated positions, the bearings, and the bearing derivatives. The proposed observer only requires the (directed) sensing topology to be angle‑rigid, a weaker condition than the commonly used ones like bearing rigidity. Local uniform exponential stability of the proposed observer is established under the assumption of persistently exciting motions for a subset of robots. Simulations are presented and discussed to evaluate the scheme's effectiveness and practicality.

Abstract:
Swarm robotics, particularly drone swarms, are used in various safety‑critical tasks. While a lot of attention has been given to improving swarm control algorithms for improved intelligence, the security implications of various design choices in swarm control algorithms have not been studied. We highlight how an attacker can exploit the vulnerabilities in swarm control algorithms to disrupt drone swarms. Specifically, we show that the attacker can target a swarm member (target drone) through GPS spoofing attacks, and indirectly cause other swarm members (victim drones) to veer from their course, resulting in collisions. We call these Swarm Propagation Vulnerabilities (SPVs). In this paper, we introduce two fuzzing tools, SwarmFuzzGraph and SwarmFuzzBinary, to efficiently find SPVs in swarm control algorithms. SwarmFuzzGraph uses a combination of graph theory and gradient‑guided optimization to find SPVs. Our evaluation on a popular swarm control algorithm shows that SwarmFuzzGraph achieves an average success rate of 48.8% in finding SPVs. However, SwarmFuzzGraph fails to find any SPVs in drone swarms with different topologies. We then propose SwarmFuzzBinary, which uses observation‑based seed scheduling and binary search to find SPVs. The evaluation shows that SwarmFuzzBinary's success rate is comparable to SwarmFuzzGraph and work in all tested algorithms.

Abstract:
Multi‑robot systems (MRS) increasingly offload compute‑intensive perception tasks to edge nodes to meet strict time‑sensitive Quality‑of‑Service (QoS) constraints. However, static task orchestration on a shared edge node can severely degrade QoS due to network latency, jitter, and edge‑resource contention. We present a pilot edge‑centric MRS testbed using Raspberry Pi nodes to evaluate a camera‑to‑manipulator pipeline under three modes: local execution, static offloading, and a QoS‑aware Adaptive Task Placement (ATP) controller. ATP scores candidate placements using a multi‑metric cost (normalized latency, CPU utilization, and switching overhead) over two‑second control windows. The closed‑loop visual servoing testbed is instrumented with sub‑millisecond clock synchronization, network emulation, and detailed monitoring of multiple metrics across nodes to capture realistic jitter. Experimental results under compute‑stress and network‑fault scenarios show that static edge offloading reduces on‑board CPU load but amplifies tail latency and deadline misses. In contrast, the QoS‑aware ATP controller, by switching task placement based on measured latency and utilization thresholds, consistently lowers deadline violations and tail latency. Overall, the results position ATP as a practical edge‑side control primitive for MRS and concrete design guidelines for Cloud‑Edge Robotics deployments within the broader cloud‑fog automation, while motivating QoS‑aware multi‑objective workload orchestration for industrial cyber‑physical systems.

Abstract:
Cooperative perception is important for autonomous driving but remains fragile when cameras and LiDAR degrade in adverse weather. We address this challenge by integrating 4D imaging radar as a weather‑robust modality into collaborative perception and introducing a Doppler‑guided spatial attention mechanism for multi‑agent fusion. Our approach extends two representative backbones: a radar‑camera pipeline where radar substitutes LiDAR, and a LiDAR‑radar pipeline where radar complements LiDAR. To support evaluation, we release radar‑augmented benchmarks, OPV2V‑R and Adver‑City‑R, with physics‑based LiDAR degradation. Experiments show strong robustness gains in fog and rain, including substantial improvements when radar replaces degraded LiDAR. Additional validation on MAN TruckScenes demonstrates transfer beyond simulation. Overall, our results highlight 4D imaging radar as a robust modality for all‑weather collaborative perception. Dataset and code are available at: https://url.fzi.de/SlimComm.

Abstract:
Safe navigation often relies on well‑defined conditions based on the shape of robots and obstacles, and can be challenging when they have irregular geometries. While Control Barrier Functions (CBFs) offer an efficient mechanism to enforce safe set forward invariance, common shape surrogates (e.g., spheres or super‑ellipsoids) either are overly conservative in unstructured scenes or require many local primitives, which inflates constraint counts and degrades real‑time performance. In this paper, we introduce a novel geometry‑aware Control Barrier Function (CBF) based on Bernstein‑Polynomial Signed Distance Fields (BP‑SDFs). It provides a unified way to represent the obstacles and robots, so as to represent the barrier function with a unified minimum distance. Benefiting from the differentiability of the Bernstein polynomials, one can easily enforce the control constraints in a closed loop. We validate the method's efficiency and performance to guarantee safety in single‑robot navigation and heterogeneous multi‑robot collision avoidance via simulations under different environments.

Abstract:
Global megatrends, such as urbanization, population growth, and emerging network solutions are accelerating the development of the Connected and Autonomous Vehicles (CAVs) industry. There are many truths, some misconceptions, and even some excitement about CAVs in the public's opinion. The main objective of the current article is to provide a comprehensive review, eliminate misconceptions, and outline the future of the network optimization aspects of autonomous vehicles by presenting various multidisciplinary methods, such as cooperative perception. Given our extensive experience with CAVs, we are aiming to share some of the insights and knowledge we have gained, along with relevant use‑cases and experiment results.

Abstract:
Natural language interfaces can simplify interaction with multi‑robot systems, especially when non‑expert users need to issue high‑level commands. Acoustic manipulation using ultrasonic phased arrays also enables contactless object handling for applications such as healthcare, laboratory automation, and precision transport. However, combining large language models (LLMs) with distributed acoustic mobile robots remains underexplored. This paper presents a decentralized framework for natural language‑driven coordination of acoustic robots for contactless object manipulation. The system converts spoken instructions into executable multi‑robot task plans using Whisper‑based speech recognition, LLM‑based semantic parsing, structured JSON task representation, and distributed scheduling. The JSON schema encodes robot assignments, temporal dependencies, spatial constraints, and synchronization requirements for sequential, parallel, and synchronized execution. The system is implemented on two TurtleBot3‑based acoustic robots, each equipped with an ultrasonic phased array for contactless object transport. Experiments were conducted in three scenarios: sequential execution, parallel multi‑robot transport, and synchronized cooperative manipulation. The system achieved task success rates of 96 percent for sequential tasks, 86 percent for parallel execution, and 70 percent for synchronized collaborative transport. These results show that natural language commands can be transformed into distributed robot actions for contactless manipulation, highlighting the potential of LLM‑driven automation for human‑robot interaction in distributed robotic systems.

Abstract:
Swarm and field robotics face significant barriers to real‑world validation due to the high cost and development time to deploy hardware. This paper introduces the ``Bionic Swarm,'' a novel system that lowers these barriers by abstracting away many of the tasks that are difficult to implement on robots but which do not contribute to the overall algorithm evaluation, giving these tasks to human users. These human users take directions from a smartphone web‑app that takes measurements from Bluetooth‑connected sensors and relays them to a centralized server. This server runs the swarm algorithm and directs actions to the human users. We evaluate this system through the experimental validation of a geotechnically‑focused search algorithm named Score‑Biased‑Search, which functions by assigning a ``score'' to each location on a reconstructed map, then biases search patterns through areas of higher expected scores, and which exhibits superlinear map reconstruction relative to the number of search agents. After presenting simulation results for the algorithm, we then apply the algorithm on the Bionic Swarm platform to validate its function in a real‑world, outdoor setting. This work demonstrates that this human‑in‑the‑loop approach significantly lowers the barrier to entry for field and swarm robotics research.

Abstract:
Unmanned aerial vehicle (UAV) swarms are increasingly deployed in vast low‑altitude applications, owing to their capabilities in distributed sensing, flexible communication, and autonomous coordination. Nevertheless, the open and highly dynamic operating environment of UAV swarms introduces serious security risks, including GPS spoofing, insider threats, and multi‑hop intrusion. These threats are aggravated by limited on‑board resources, frequently changing network topology, and the presence of intelligent adversaries. To tackle these issues, this paper proposes a cloud‑edge‑end collaborative defense framework for UAV swarms. Based on this framework, three complementary mechanisms are developed. First, a cooperative perception scheme is designed to resist GPS spoofing via interactive attack‑defense game modeling. Second, a behavior‑driven authentication method with trust evaluation is developed to mitigate insider threats. Third, a multi‑agent attack forensics framework is devised to intelligently trace the propagation paths of multi‑hop attacks in UAV networks. Experimental results validate the effectiveness of the proposed approaches. Finally, several open research directions are outlined.

Abstract:
Multi‑robot task allocation usually assumes some combination of communication, known task models, or a coordinator. We study the opposite extreme, a regime common in practice but overlooked in theory, which we name Zero‑Knowledge MRTA (ZK‑MRTA): a robot team with no prior knowledge (no task models, not even the latent rank), no communication (no messages, no parameter sharing, no coordinator), and only a partial and privately‑noisy view of a public stream of teammates' outcomes. A hidden low‑rank structure governs which robot suits which task, and there are far more tasks than rounds, so most (robot, task) pairs are never attempted. Yet each robot can act well on tasks it never attempted, and onboard new tasks, by running online low‑rank collaborative filtering over the broadcast (SwarmCF). The advantage over any structure‑free learner is categorical, not a constant factor: a structure‑free learner is provably at the prior‑mean error floor on unseen pairs. We prove a matching per‑robot sample complexity (Θ(d) versus Θ(n), in the rank d and the task count n), an anytime (cumulative‑reward) separation under task scarcity, and a deterministic condition under which decentralized recovery from the masked broadcast is exact (validated empirically). Experiments quantify the value of the broadcast, a positive scaling law (per‑robot unseen‑pair skill rises with team size), and the strongest masking‑robustness and anytime profile among low‑rank methods, recovering most (about 80% on earned skill) of a centralized full‑communication ceiling, and holding under capacity‑1 contention and in a robotics‑grounded sensing instance.

Abstract:
We consider the problem of combining potential field and ergodic search on multi‑robot systems. Traditional ergodic search algorithms use metrics for ergodicity that account for the desired distribution at different scales. Recently, a heat equation‑driven ergodic approach was proposed, which adds flexibility to the smoothing of the ergodic metric. However, such an approach, as it is an isotropic diffusion, propagates the error uniformly in all directions, regardless of changes in the desired distribution. We introduce a general class of anisotropic diffusion formulation of the ergodicity problem, which generates a potential field for the ergodic search. We demonstrate that this approach generalizes previous results, which consider radial basis functions and the solution of the heat equation to represent the difference between the goal density distribution and the covered trajectories. In our solution, the agent movement is directed using the gradient of the solution of the Perona‑Malik diffusion, and our formulation includes the heat equation as a special case. We demonstrate the methodology with a series of simulations in different scenarios.

Abstract:
Multi‑Robot Task Allocation (MRTA) is a central challenge in decentralized multi‑agent systems, where teams of robots must cooperatively assign and execute tasks under limited communication while optimizing global performance objectives. Auction‑consensus algorithms, such as the Consensus‑Based Bundle Algorithm (CBBA), provide scalable decentralized coordination with provable convergence, but rely on hand‑crafted greedy scoring functions that often lead to suboptimal task allocations. This paper proposes a learning‑enhanced auction‑consensus framework in which CBBA's deterministic bidding mechanism is replaced by a neural bidding policy trained using reinforcement learning. Under a centralized training and decentralized execution paradigm, agents learn to compute task bids from partial local observations while retaining the standard auction and consensus phases for decentralized coordination. The learned bidding policy is trained using Proximal Policy Optimization with rewards shaped by proximity to globally optimal solutions obtained via mixed‑integer linear programming. Multiple neural architectures are evaluated, including a Neural Additive Model, the Long Short‑Term Memory (LSTM) model, and the Set Transformer Model. Experimental results across varying swarm sizes demonstrate that learned bidding policies can improve solution quality over classical CBBA while preserving decentralized execution. The proposed approach highlights the effectiveness of integrating reinforcement learning with classical distributed coordination algorithms, offering a scalable pathway toward higher‑quality decentralized multi‑robot task allocation.

Abstract:
In communicationless environments, multi‑robot systems must operate without the constant information exchange that many coordination strategies typically assume. This paper presents a novel dynamic epistemic planning framework that enables implicit coordination and long horizon planning through higher‑order reasoning among robots. With our approach, robots form and propagate higher‑order belief particles, update world beliefs using Bayesian inference, and select actions via a behavior tree that anticipates teammates' likely decisions. A temporally aware Model Predictive Path Integral (MPPI) controller integrates this reasoning into low‑level execution, allowing robots to plan intercepts and adapt trajectories under partial observability. The proposed framework is evaluated in both simulations and physical experiments, where it consistently reduces task completion time compared to a first‑order baseline, demonstrating that epistemic logic can serve as a robust foundation for resilient coordination in communication‑restricted domains.

Abstract:
Dense low‑altitude aerial operations require more than pre‑flight route coordination and last‑resort collision avoidance. Once aircraft are airborne, disturbances can emerge on timescales shorter than strategic reauthorization can absorb, while collision avoidance is too late and disruptive to serve as routine traffic management. Although tactical separation is recognized as the intermediate layer, realizing it at scale requires a deployable neighborhood communication mechanism that provides fresh, trusted information for local coordination. This paper presents what is, to our knowledge, the first controller‑coupled characterization of an all‑airborne, sidelink‑class, intent‑first vehicle‑to‑vehicle (V2V) tactical neighborhood exchange stack for dense Unmanned Aircraft System Traffic Management (UTM) operations. Unlike awareness‑only broadcast, the proposed exchange combines refreshed state and intent beacons for local awareness, cooperative perception, and degraded‑mode assessment with event‑triggered messages for yielding, sequencing, release, and contingency coordination. We implement and evaluate this model on an all‑airborne V2V stack using sidelink‑class C‑V2X modules with authenticated freshness checks. Evaluation uses a scenario‑driven, high‑volume stress campaign supported by real‑time, field‑anchored infrastructure. Results show that V2V reduces stale‑belief divergence, preserves observability through cooperative perception, rejects invalid tactical messages, suppresses false local inference, and structures shared‑resource coordination. The implemented stack provides a viable communication layer for tactical separation in lower‑to‑moderate regimes, but transitions toward guarded fallback as density, impairment, and complexity increase. These findings position intent‑first aerial V2V as a bounded enabler for scaling tactical coordination in disturbance‑driven urban airspace.

Abstract:
This paper presents a learning‑augmented trajectory planning framework for cooperative unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) handover missions. While centralized trajectory optimization ensures dynamic feasibility and task optimality, its high computational cost limits real‑time applicability. We propose a neural surrogate planner utilizing decoupled encoder‑decoder long short‑term memory (LSTM) networks to generate coordinated handover trajectory predictions from the task specifications. These predictions serve as informed warm starts for the downstream centralized optimizer, thereby accelerating convergence to dynamically feasible solutions. Benchmark evaluations demonstrate that the learning‑augmented planning framework achieves more than a threefold speedup and 100% optimization success rate compared to cold start optimization. The results indicate that combining data‑driven inference with model‑based refinement enables fast and reliable trajectory generation for heterogeneous multi‑robot systems.

Abstract:
Operating a multi‑robot fleet for simultaneous localization and mapping (SLAM) in applications such as building inspection or warehouse‑aisle monitoring requires the operator to maintain spatial awareness of each robot's position and mapping state, a task that scales poorly on conventional 2D interfaces. We present MR‑SLAM, a mixed reality (MR) system in which an operator wearing a Meta Quest 3 headset teleoperates three simulated TurtleBot3 robots through a passthrough view with real‑world occlusion, while spatially anchored dashboard panels report mapping progress in situ. Each robot runs an independent SLAM Toolbox instance whose occupancy grid is merged in real time on a Robot Operating System 2 (ROS 2) back end. Across five 9‑minute evaluation sessions, the system delivered scans at 8.83 +/‑ 0.16 Hz, mapped 17.9 +/‑ 0.8 m^2 of merged occupancy, and reached 94.7 +/‑ 0.5% cross‑instance occupancy consistency across robot pairs. An additional session recorded 6.3 ms median transform jitter and 26.7 m^2 coverage of a 41 m^2 grid. We position MR‑SLAM as a reference implementation for combining passthrough mixed reality supervision with multi‑robot SLAM on consumer hardware.

Abstract:
Multi‑robot systems are integral to modern logistics, but their capabilities are often limited to tasks executable by individual agents. This paper addresses a critical gap in existing frameworks like Multi‑Agent Path Finding (MAPF) and Task Allocation and Path Finding (TAPF), which lack true cooperation for transporting large items that require multiple agents. To this end, we formalise the Cooperative Transportation Task Allocation and Path Finding (CT‑TAPF) problem, which integrates team formation, task assignment, and collision‑free pathfinding. We present an optimal solver, Cooperative Transportation Task Conflict‑Based Search (CT‑TCBS), which features a novel Incremental Expansion strategy to tackle the combinatorial explosion inherent in team formation. Recognising the computational cost of optimality, we also develop a family of sub‑optimal solvers that employ a global, task‑centric perspective, selecting the next task to assign based on a global difficulty metric (Best Task or Worst Task). Our comprehensive empirical evaluation demonstrates three key findings: (1) the incremental expansion strategy significantly outperforms the naive combinatorial approach by successfully pruning the dominant task‑allocation search space; (2) we identify a task‑conflict expansion dilemma, where sophisticated conflict resolvers effective for large‑agent pathfinding subproblems can be detrimental in the integrated CT‑TAPF setting; and (3) our proposed sub‑optimal solvers establish a new, more efficient frontier on the solution quality‑runtime spectrum compared to "nn‑" agent‑centric baselines. This work provides a foundational framework and a set of effective algorithms for a new, practical class of cooperative multi‑agent problems.

Abstract:
This paper presents a prototyping framework for distributed control of multi‑robot systems, aimed at bridging theory and practical testing of distributed optimization algorithms. Using the Single Program, Multiple Data (SPMD) paradigm, the framework emulates distributed control on a single computer, with each core running the same algorithm using local states and neighbour‑to‑neighbour communication. We demonstrate the framework on a four‑quadrotor position‑swapping task using a non‑cooperative game‑theoretic distributed algorithm. Computational time and trajectory data are compared across the supported dynamics levels: a point‑mass model, a high‑fidelity quadrotor model, and an experimental hardware testbed using Crazyflie quadcopters. The results show that the framework provides a low‑cost and accessible approach for validating distributed algorithms.

Abstract:
This paper studies multi‑agent reinforcement learning with submodular team utilities for online distributed task allocation. In this setting, each agent selects one action from a local categorical policy, so feasible joint actions form a partition matroid over agent‑action pairs. Classical multilinear extensions use independent Bernoulli sampling and therefore do not match the categorical policies executed by decentralized agents. To address this mismatch, we introduce the Partition Multilinear Extension (PME), a continuous relaxation whose value equals the expected team utility under factorized categorical policies. We prove that submodular difference rewards provide unbiased PME marginal‑gradient information and yield a stagewise score‑function policy‑gradient estimator. Based on this connection, we propose SubMAPG, a centralized‑training decentralized‑execution policy‑gradient framework with masked categorical policies and submodular difference‑reward training signals. For the associated PME marginal‑space projected stochastic‑gradient dynamics, we prove a stagewise 1/2‑approximation guarantee and sublinear dynamic regret in slowly varying environments, measured by the path length of the optimal PME marginals. To handle open systems with time‑varying agents and targets, we instantiate SubMAPG with graph neural network policies. Experiments on multi‑robot coverage and multi‑target tracking show that SubMAPG outperforms local greedy and shared‑reward baselines and is competitive with centralized myopic greedy strategies.

Abstract:
Swarm robotics utilises decentralised self‑organising systems to form complex collective behaviours built from the bottom‑up using individuals that have limited capabilities. Previous work has shown that simple occlusion‑based strategies can be effective in using swarm robotics for the task of transporting objects to a goal position. However, this strategy requires a clear line‑of‑sight between the object and the goal. In this paper, we extend this strategy by allowing robots to form sub‑goals; enabling any member of the swarm to establish a wider range of visibility of the goal, ultimately forming a chain of sub‑goals between the object and the goal position. We do so while preserving the fully decentralised and communication‑free nature of the original strategy, while maintaining performance in object‑free scenarios. In five sets of simulated experiments, we demonstrate the generalisability of our proposed strategy. Our finite‑state machine allows a sufficiently large swarm to transport objects around obstacles that block the goal. The method is robust to varying starting positions and can handle both concave and convex shapes.

Abstract:
Multi‑robot simultaneous localization and mapping (SLAM) is a fundamental task in multi‑robot operations. Robots must have a common understanding of their location and that of their team members to complete coordinated actions. However, multi‑robot SLAM between Uncrewed Surface Vessels (USVs) and Autonomous Underwater Vehicles (AUVs) has primarily been achieved through acoustic pinging between robots to retrieve range measurements; a measurement technique requires that robots to be in similar locations simultaneously, have an uninterrupted path for signal propagation, and may necessitate synchronized clocks. This is especially challenging in complex, cluttered maritime environments, where structures may impede signals. However, these same structures may be observable above and below the water's surface, presenting an opportunity for inter‑robot SLAM loop closure between USV and AUV data streams. This work builds upon recent research on inter‑robot SLAM loop closure between USV and AUV data, extending it to propose a centralized multi‑robot SLAM system. Each robot performs its state estimation, and we detect loop closures between each AUV and the USV data. These inter‑robot loop closures are used to merge each robot's state estimate into a centralized graph, yielding estimates for the whole time history of the USV and all AUVs in the system. Validation is performed using real‑world perceptual data in three different environments. Results show improved errors for AUVs in the multi‑robot SLAM system compared to single‑robot SLAM over the same trajectories. To our knowledge, this is the first instance of a multi‑robot SLAM system with AUVs and USVs built on loop closures rather than acoustic distance measurements.

Abstract:
Multi‑robot systems in automated warehouses must manage continuous streams of pickup‑and‑delivery tasks while ensuring efficiency and safety. Prior work on Multi‑Agent Pickup‑and‑Delivery (MAPD) has largely focused on the one‑to‑one variant, where each task has a fixed pickup and delivery location. In contrast, real warehouses often present many‑to‑many MAPD scenarios, where items, tracked by stock keeping unit (SKU) identifiers, can be retrieved from or stored at multiple locations, resulting in an NP‑hard four‑dimensional assignment problem. To solve the many‑to‑many MAPD problem, we contribute our algorithm: Many‑to‑Many Multi‑Agent Pickup and Delivery (M2M). We experiment with two variants of our algorithm: one that minimizes estimated task durations (M2M), and one which incorporates SKU distribution into the objective function (M2M‑wSKU). Simulation results over 8‑hour warehouse operations show that our method consistently matches or outperforms prior state of the art, with M2M completing up to 22,000 more tasks on average across different environments and warehouse inventory densities.

Abstract:
Natural‑language interfaces can make swarm robotics more accessible to non‑expert operators, but they must translate ambiguous user intent into executable swarm behaviors without unsupported actions, malformed programs, or unsafe plans. This paper presents CommandSwarm, a safety‑aware language‑to‑behavior‑tree pipeline for generating XML behavior trees (BTs) from speech or text commands. The system combines multilingual translation, command‑level safety filtering, constrained prompting, a LoRA‑adapted large language model (LLM), and deterministic parser validation against a whitelist of executable swarm primitives. We evaluate eleven open 6.7B‑‑14B parameter LLMs, all using 4‑bit quantization, on representative swarm‑control scenarios under zero‑shot, one‑shot, and two‑shot prompting. Falcon3‑Instruct‑10B and Mistral‑7B‑v3 are the strongest prompt‑engineered candidates, reaching BLEU scores above 0.60 and high syntactic validity in few‑shot settings. LoRA adaptation of Falcon3‑Instruct‑10B on a 2,063‑example synthetic instruction‑‑BT corpus improves zero‑shot BLEU from 0.267 to 0.663, ROUGE‑L from 0.366 to 0.692, and parser‑accepted syntactic validity from 0% to 72%. Translation experiments further show that SeamlessM4T v2‑large and EuroLLM‑9B provide the best quality‑latency trade‑offs for the multilingual front end. The results indicate that compact, quantized, domain‑adapted LLMs can generate useful swarm BTs when embedded in a validated systems pipeline. They also show that parser acceptance and safety filtering remain necessary execution gates; generation quality alone is not sufficient for autonomous deployment.

Abstract:
Event cameras offer high temporal resolution, low latency, and wide dynamic range, making them promising receivers for visible light communication (VLC) in vehicle‑to‑everything (V2X) applications. This work presents an event‑camera‑based VLC system addressing three key challenges: bandwidth saturation, multi‑transmitter reception, and latency characterization. We adopt a positive‑event‑only mode and design a protocol that suppresses event generation while maintaining communication distance and a wide field of view. We also propose a method to identify multiple transmitters and demonstrate simultaneous reception from up to three LEDs. Finally, we evaluate end‑to‑end latency in real vehicular scenarios and show that the system meets cooperative perception requirements. These results demonstrate that event‑camera‑based VLC is a feasible complement to existing V2X technologies (e.g., RF).

Abstract:
In the field of precision manufacturing in complex constrained environments, the role of soft robots is increasingly prominent, and the realization of anti‑winding control based on multi‑intelligent body reinforcement learning has become a research hotspot. One of the core problems at present is to coordinate multiple robots to complete the unwinding operation in a highly constrained environment. The existing distributed training framework faces some observability challenges in high‑density barrier and unstable environments, resulting in poor learning results. This paper proposes a topology‑driven Multi‑Agent Reinforcement Learning (TD‑MARL) framework to coordinate multi‑robot systems to avoid entanglement. Specifically, the critical network adopts centralized learning, so that each intelligent body can perceive the strategies of other intelligent bodies by sharing the topological state, thus alleviating the training instability caused by complex interactions; eliminating the demand for communication resources between robots through distributed execution, Upgrade system reliability; the integrated topological security layer uses topological invariants to accurately assess and mitigate the risk of entanglement to avoid the strategy from falling into local difficulties. Finally, the full simulation experiments carried out in the real simulation environment show that the method is better than the current advanced deep reinforcement learning (DRL) method in terms of convergence and anti‑winding effect.

Abstract:
Accurate 3D object detection is essential for ensuring the safety of autonomous vehicles. Cooperative perception, which leverages vehicle‑to‑everything (V2X) communication to share perceptual data, enhances detection but is vulnerable to channel impairments, such as noise, fading, and interference. To strengthen the reliability of intelligent transportation systems, this work improves the robustness of V2X cooperative perception under communication conditions that reflect common channel impairments. This paper proposes an Adaptive Feature Fusion Transformer (AFFormer), a Transformer‑based framework that mitigates the adverse effects of corrupted features by modeling temporal, inter‑agent, and spatial correlations. AFFormer introduces three key modules: Multi‑Agent and Temporal Aggregation for context‑aware fusion across agents and over time, Dual Spatial Attention for efficient modeling of spatial dependencies, and Uncertainty‑Guided Fusion for entropy‑driven refinement of fused features. A teacher‑student knowledge distillation strategy further enhances robustness by aligning fused features with reliable early‑collaboration supervision. AFFormer is validated on the V2XSet and DAIR‑V2X datasets, where it consistently outperforms existing methods under both ideal and impaired communication conditions, demonstrating improved robustness to communication‑induced feature degradation while maintaining a competitive efficiency‑accuracy trade‑off.

Abstract:
Patrolling with multiple robots offers efficient surveillance to detect and manage undesired situations. This necessitates improved patrol efficiency and operator situation awareness at base stations. Enhanced situation awareness enables operators to predict robots' behaviors, support recognition and decision‑making, and execute emergency interventions. This study presents the Local Reactive and Partition (LR‑PT) algorithm, a novel multi‑robot patrolling approach. In simulations, LR‑PT outperformed existing methods by ensuring frequent patrols of all locations of interest and enhancing the situation awareness of the base station. Robots independently select patrol targets based on locally available information, integrating patrol needs and the urgency of reporting mission progress to the base station into a unified utility function. This locality also contributes to robustness against communication constraints and robot failures, as demonstrated in this research. The algorithm further autonomously emerged the area partition, which can avoid falling into local optima and realize the comprehensive patrol over the whole mission area. The simulation results demonstrated the superior performance of LR‑PT for multi‑robot patrolling, utilizing the advantages of swarm robotics and addressing real‑world operational challenges.

Abstract:
Heterogeneous multi‑robot systems feature significant adaptability for complex environments. However, effective collaboration that fully exploits the robots' potential remains a core challenge. This paper proposes a decentralized collaborative framework for heterogeneous multi‑robot systems to autonomously explore indoor and outdoor 3D environments. First, a basic perception map that integrates terrain and observation metrics is designed. Improved supervoxel segmentation is developed to simplify the map structure and form a high‑level representation that supports lightweight communication. Second, the traversal and observation capabilities of heterogeneous robots are modeled to evaluate the requirements of task views derived from incomplete supervoxels. These task views are grouped by requirements and clustered to streamline assignment. Subsequently, the view‑cluster assignment is formulated as a heterogeneous multi‑depot multi‑traveling salesman problem (HMDMTSP) that incorporates constraints between view‑cluster requirements and robot capabilities. An improved genetic algorithm is developed to efficiently solve this problem while ensuring global consistency. Based on the assignments, redundant views within clusters are eliminated to refine exploration routes. Finally, conflicts between robots' motion paths are resolved. Simulations and field experiments in cluttered indoor and outdoor environments demonstrate that our approach effectively coordinates exploration tasks among heterogeneous robots, achieving superior exploration efficiency and communication savings compared to state‑of‑the‑art approaches.

Abstract:
This letter presents an energy‑efficient multi‑robot coverage path planning (MRCPP) framework for large, nonconvex Regions of Interest (ROI) containing obstacles and no‑fly zones (NFZ). Existing minimum‑energy coverage planning algorithms utilize meta‑heuristic boustrophedon workspace decomposition. Therefore, even with minimum energy objectives and energy consumption constraints, they cannot achieve optimal energy efficiency. Moreover, most existing frameworks support only a single type of robotic platform. MRCPP overcomes these limitations by: generating globally‑informed swath generation, creating parallel sweeping paths with minimal turns, calculating safety buffers to ensure safe turning clearance, using an efficient mTSP solver to balance workloads and minimize mission time, and connecting disjoint segments via a modified visibility graph that tracks heading angles while maintaining transitions within safe regions. The efficacy of the proposed MRCPP framework is demonstrated through real‑world experiments involving autonomous aerial vehicles (AAVs) and autonomous surface vehicles (ASVs). Evaluations demonstrate that the proposed MRCPP consistently outperforms state‑of‑the‑art planners, reducing average total energy consumption by 3% to 40% for a team of 3 robots and computation time by an order of magnitude, while maintaining balanced workload distribution and strong scalability across increasing fleet sizes. The MRCPP framework is released as an open‑source package and videos of real‑world and simulated experiments are available at https://mrc‑pp.github.io.

Abstract:
Designing multi‑agent robotic systems requires reasoning across tightly coupled decisions spanning heterogeneous domains, including robot design, fleet composition, and planning. Much effort has been devoted to isolated improvements in these domains, whereas system‑level co‑design considering trade‑offs and task requirements remains underexplored. In this work, we present a formal and compositional framework for the task‑driven co‑design of heterogeneous multi‑robot systems. Building on a monotone co‑design theory, we introduce general abstractions of robots, fleets, planners, executors, and evaluators as interconnected design problems with well‑defined interfaces that are agnostic to both implementations and tasks. This structure enables efficient joint optimization of robot design, fleet composition, and planning under task‑specific performance constraints. A series of case studies demonstrates the capabilities of the framework. Various component models can be seamlessly incorporated, including new robot types, task profiles, and probabilistic sensing objectives, while non‑obvious design alternatives are systematically uncovered with optimality guarantees. The results highlight the flexibility, scalability, and interpretability of the proposed approach, and illustrate how formal co‑design enables principled reasoning about complex heterogeneous multi‑robot systems.

Abstract:
Discrete‑event systems and supervisory control theory provide a rigorous framework for specifying correct‑by‑construction behavior. However, their practical application to swarm robotics remains largely underexplored. In this paper, we investigate a topological recovery method based on discrete‑event‑systems within a swarm robotics context. We propose a hybrid architecture that combines a high‑level discrete event systems supervisor with a low‑level continuous controller, allowing lost drones to safely recover from fault or attack events and re‑enter a controlled region. The method is demonstrated using ten simulated UAVs in the py‑bullet‑drones framework. We show recovery performance across four distinct scenarios, each with varying initial state estimates. Additionally, we introduce a secondary recovery supervisor that manages the regrouping process for a drone after it has re‑entered the operational region.

Abstract:
In this paper, we aim to extend the traditional point‑mass‑like robot representation in swarm robotics and instead study a swarm of long Heavy Articulated Vehicles (HAVs). HAVs are kinematically constrained, elongated, and articulated, introducing unique challenges. Local, decentralized coordination of these vehicles is motivated by many real‑world applications. Our approach, Prevent‑Jack, introduces the sparsely covered context steering framework in robotics. It fuses six local behaviors, providing guarantees against jackknifing and collisions at the cost of potential dead‑ and livelocks, tested for vehicles with up to ten trailers. We highlight the importance of the Evade Attraction behavior for deadlock prevention using a parameter study, and use 15,000 simulations to evaluate the swarm performance. Our extensive experiments and the results show that both the dead‑ and livelocks occur more frequently in larger swarms and denser scenarios, affecting a peak average of 27%/31% of vehicles. We observe that larger swarms exhibit increased waiting, while smaller swarms show increased evasion.

Abstract:
Autonomous swarms of multi‑Unmanned Aerial Vehicle (UAV) system requires an accurate and fast relative state estimation. Although monocular frame‑based camera methods perform well in ideal conditions, they are slow, suffer scale ambiguity, and often struggle in visually challenging conditions. The advent of event cameras addresses these challenging tasks by providing low latency, high dynamic range, and microsecond‑level temporal resolution. This paper proposes a framework for relative state estimation for quadrotors using event‑based propeller sensing. The propellers in the event stream are tracked by detection to extract the region‑of‑interests. The event streams in these regions are processed in temporal chunks to estimate per‑propeller frequencies. These frequency measurements drive a kinematic state estimation module as a thrust input, while camera‑derived position measurements provide the update step. Additionally, we use geometric primitives derived from event streams to estimate the orientation of the quadrotor by fitting an ellipse over a propeller and backprojecting it to recover body‑frame tilt‑axis. The existing event‑based approaches for quadrotor state estimation use the propeller frequency in simulated flight sequences. Our approach estimates the propeller frequency under 3% error on a test dataset of five real‑world outdoor flight sequences, providing a method for decentralized relative localization for multi‑robot systems using event camera.

Abstract:
Coordinating multi‑robot systems (MRS) to search in unknown environments is particularly challenging for tasks that require semantic reasoning beyond geometric exploration. Classical coordination strategies rely on frontier coverage or information gain and cannot incorporate high‑level task intent, such as searching for objects associated with specific room types. We propose Semantic Area Graph Reasoning (SAGR), a hierarchical framework that enables Large Language Models (LLMs) to coordinate multi‑robot exploration and semantic search through a structured semantic‑topological abstraction of the environment. SAGR incrementally constructs a semantic area graph from a semantic occupancy map, encoding room instances, connectivity, frontier availability, and robot states into a compact task‑relevant representation for LLM reasoning. The LLM performs high‑level semantic room assignment based on spatial structure and task context, while deterministic frontier planning and local navigation handle geometric execution within assigned rooms. Experiments on the Habitat‑Matterport3D dataset across 100 scenarios show that SAGR remains competitive with state‑of‑the‑art exploration methods while consistently improving semantic target search efficiency, with up to 18.8% in large environments. These results highlight the value of structured semantic abstractions as an effective interface between LLM‑based reasoning and multi‑robot coordination in complex indoor environments.

Abstract:
This paper presents a novel density control framework for multi‑robot systems with spatial safety and energy sustainability guarantees. Stochastic robot motion is encoded through the Fokker‑Planck Partial Differential Equation (PDE) at the density level. Control Lyapunov and control barrier functions are integrated with PDEs to enforce target density tracking, obstacle region avoidance, and energy sufficiency over multiple charging cycles. The resulting quadratic program enables fast in‑the‑loop implementation that adjusts commands in real‑time. Multi‑robot experiment and extensive simulations were conducted to demonstrate the effectiveness of the controller under localization and motion uncertainties.

Abstract:
Autonomous vehicles equipped with robust onboard perception, localization, and planning still face limitations in occlusion and non‑line‑of‑sight (NLOS) scenarios, where delayed reactions can increase collision risk. We propose CooperDrive, a cooperative perception framework that augments situational awareness and enables earlier, safer driving decisions. CooperDrive offers two key advantages: (i) each vehicle retains its native perception, localization, and planning stack, and (ii) a lightweight object‑level sharing and fusion strategy bridges perception and planning. Specifically, CooperDrive reuses detector Bird's‑Eye View (BEV) features to estimate accurate vehicle poses without additional heavy encoders, thereby reconstructing BEV representations and feeding the planner with low latency. On the planning side, CooperDrive leverages the expanded object set to anticipate potential conflicts earlier and adjust speed and trajectory proactively, thereby transforming reactive behaviors into predictive and safer driving decisions. Real‑world closed‑loop tests at occlusion‑heavy NLOS intersections demonstrate that CooperDrive increases reaction lead time, minimum time‑to‑collision (TTC), and stopping margin, while requiring only 90 kbps bandwidth and maintaining an average end‑to‑end latency of 89 ms.

Abstract:
Safe navigation for multi‑robot systems requires enforcing safety without sacrificing task efficiency under decentralized decision‑making. Existing decentralized methods often assume robot homogeneity, making shared safety requirements non‑uniformly interpreted across heterogeneous agents with structurally different dynamics, which could lead to avoidance obligations not physically realizable for some robots and thus cause safety violations or deadlock. In this paper, we propose Capability‑Aware Heterogeneous Control Barrier Function (CA‑HCBF), a decentralized framework for consistent safety enforcement and capability‑aware coordination in heterogeneous robot teams. We derive a canonical second‑order control‑affine representation that unifies holonomic and nonholonomic robots under acceleration‑level control via canonical transformation and backstepping, preserving forward invariance of the safe set while avoiding relative‑degree mismatch across heterogeneous dynamics. We further introduce a support‑function‑based directional capability metric that quantifies each robot's ability to follow its motion intent, deriving a pairwise responsibility allocation that distributes the safety burden proportionally to each robot's motion capability. A feasibility‑aware clipping mechanism further constrains the allocation to each agent's physically achievable range, mitigating infeasible constraint assignments common in dense decentralized CBF settings. Simulations with up to 30 heterogeneous robots and a physical multi‑robot demonstration show improved safety and task efficiency over baselines, validating real‑world applicability across robots with distinct kinematic constraints.

Abstract:
Long‑horizon collaborative vision‑language navigation (VLN) is critical for multi‑robot systems to accomplish complex tasks beyond the capability of a single agent. CoNavBench takes a first step by introducing the first collaborative long‑horizon VLN benchmark with relay‑style multi‑robot tasks, a collaboration taxonomy, along with graph‑grounded generation and evaluation to model handoffs and rendezvous in shared environments. However, existing benchmarks and evaluations often do not enforce strictly synchronized dual‑robot rollout on a shared world timeline, and they typically rely on static coordination policies that cannot adapt when new cross‑agent evidence emerges. We present Dialog enhanced Long‑Horizon Collaborative Vision‑Language Navigation (DeCoNav), a decentralized framework that couples event‑triggered dialogue with dynamic task allocation and replanning for real‑time, adaptive coordination. In DeCoNav, robots exchange compact semantic states via dialogue without a central controller. When informative events such as new evidence, uncertainty, or conflicts arise, dialogue is triggered to dynamically reassign subgoals and replan under synchronized execution. Implemented in DeCoNavBench with 1,213 tasks across 176 HM3D scenes, DeCoNav improves the both‑success rate (BSR) by 69.2%, demonstrating the effectiveness of dialogue‑driven, dynamically reallocated planning for multi‑robot collaboration.

Abstract:
We study dynamic multi‑robot task allocation under uncertain task completion, time‑window constraints, and incomplete information. Tasks arrive online over a finite horizon and must be completed within specified deadlines, while agents operate from distributed hubs with limited sensing and communication. We model incomplete information through hub‑based sensing regions that determine task visibility and a communication graph that governs inter‑hub information exchange. Using this framework, we propose Iterative Best Response (IBR), a decentralized policy in which each agent selects the task that maximizes its marginal contribution to the locally observed welfare. We compare IBR against three baselines: Earliest Due Date first (EDD), Hungarian algorithm, and Stochastic Conflict‑Based Allocation (SCoBA), on a city‑scale package‑delivery domain with up to 100 drones and varying task arrival scenarios. Under full and sparse communication, IBR achieves competitive task‑completion performance with lower computation time.

Abstract:
Urban intersections expose the limitations of single‑vehicle perception under occlusion and partial observability. In this study, we present an auditable roadside LiDAR framework for infrastructure‑assisted safety analysis at a signalized urban intersection in New York City, developed and evaluated using real‑world data. The proposed framework integrates trajectory construction, iterative human‑in‑the‑loop quality assurance (QA), and interpretable near‑miss analytics to produce defensible safety evidence from infrastructure sensing. Using a human‑labeled heavy vehicle‑‑bicycle interaction as an anchor case, we show that direction‑agnostic time‑to‑collision (TTC) drops below 1s, while longitudinal TTC remains above conservative braking thresholds, revealing a lateral‑intrusion‑dominated conflict mechanism. Beyond individual cases, continuous‑window evaluation and multi‑round QA analysis demonstrate that the framework systematically reduces failure modes such as track fragmentation, spurious TTC triggers, unstable geometry, and cross‑lane false conflicts. These results position roadside LiDAR as a practical post‑hoc auditing mechanism for cooperative perception systems, with broader statistical validation discussed. This work provides a pathway toward scalable, data‑driven safety auditing of urban intersections, enabling transportation agencies to identify and mitigate high‑risk interactions beyond crash‑based analyses.

Abstract:
Cooperative perception allows connected vehicles and roadside infrastructure to share sensor observations, creating a fused scene representation beyond the capability of any single platform. However, most cooperative 3D object detectors use a uniform fusion strategy for all object classes, which limits their ability to handle the different geometric structures and point‑sampling patterns of small and large objects. This problem is further reinforced by narrow evaluation protocols that often emphasize a single dominant class or only a few cooperation settings, leaving robust multi‑class detection across diverse vehicle‑to‑everything interactions insufficiently explored. To address this gap, we propose a class‑adaptive cooperative perception architecture for multi‑class 3D object detection from LiDAR data. The model integrates four components: multi‑scale window attention with learned scale routing for spatially adaptive feature extraction, a class‑specific fusion module that separates small and large objects into attentive fusion pathways, bird's‑eye‑view enhancement through parallel dilated convolution and channel recalibration for richer contextual representation, and class‑balanced objective weighting to reduce bias toward frequent categories. Experiments on the V2X‑Real benchmark cover vehicle‑centric, infrastructure‑centric, vehicle‑to‑vehicle, infrastructure‑to‑infrastructure, and vehicle‑to‑infrastructure settings under identical backbone and training configurations. The proposed method consistently improves mean detection performance over strong intermediate‑fusion baselines, with the largest gains on trucks, clear improvements on pedestrians, and competitive results on cars. These results show that aligning feature extraction and fusion with class‑dependent geometry and point density leads to more balanced cooperative perception in realistic vehicle‑to‑everything deployments.

Abstract:
Integrated Sensing and Communications (ISAC) enables trajectory sharing that enhances beamforming, resource allocation, and cooperative perception, yet raises fundamental privacy concerns under the General Data Protection Regulation (GDPR) data minimisation principle. This paper proposes a Fisher Information Density (FID)‑constrained trajectory sharing framework that enforces a local lower bound on estimation uncertainty, providing hard, quantifiable privacy guarantees by construction. Unlike fixed‑noise approaches, the proposed method bounds the Privacy Leak Ratio (PLR) regardless of sensing power or adversarial post‑processing, ensuring that no trajectory segment can be reconstructed beyond a prescribed accuracy threshold. Simulations on the OpenTraj dataset demonstrate that the framework keeps the average PLR below 20‑25% and the maximum leakage segment duration under 2‑2.5 s, while preserving data utility for downstream tasks such as movement prediction. The resulting criterion is interpretable, model‑agnostic, and compatible with GDPR‑compliant ISAC system design.

Abstract:
Multi‑robot systems are becoming increasingly relevant within diverse application domains, such as healthcare, exploration, and rescue missions. However, building such systems is still a significant challenge, since it adds the complexities of the physical nature of robots and their environments to those inherent in coordinating any distributed (multi‑agent) system. Aggregate Programming (AP) has recently emerged as a promising approach to engineering resilient, distributed systems with proximity‑based communication, and is notably supported by practical frameworks. In this paper we present a prototype of a multi‑robot service system, which adopts AP for the design and implementation of its coordination software. The prototype has been validated both with simulations, and with tests in a University library.

Abstract:
Coordinating robotic swarms in dynamic and communication‑constrained environments remains a fundamental challenge for collective intelligence. This paper presents a novel framework for event‑triggered organization, designed to achieve highly efficient and adaptive task allocation in a heterogeneous robotic swarm. Our approach is based on an adaptive consensus mechanism where communication for task negotiation is initiated only in response to significant events, eliminating unnecessary interactions. Furthermore, the swarm self‑regulates its coordination pace based on the level of environmental conflict, and individual agent resilience is managed through a robust execution model based on Behavior Trees. This integrated architecture results in a collective system that is not only effective but also remarkably efficient and adaptive. We validate our framework through extensive simulations, benchmarking its performance against a range of coordination strategies. These include a non‑communicating reactive behavior, a simple information‑sharing protocol, the baseline Consensus‑Based Bundle Algorithm (CBBA), and a periodic CBBA variant integrated within a Behavior Tree architecture. Furthermore, our approach is compared with Clustering‑CBBA (C‑CBBA), a state‑of‑the‑art algorithm recognized for communication‑efficient task management in heterogeneous clusters. Experimental results demonstrate that the proposed method significantly reduces network overhead when compared to communication‑heavy strategies. Moreover, it maintains top‑tier mission effectiveness regarding the number of tasks completed, showcasing high efficiency and practicality. The framework also exhibits significant resilience to both action execution and permanent agent failures, highlighting the effectiveness of our event‑triggered model for designing adaptive and resource‑efficient robotic swarms for complex scenarios.

Abstract:
Learning based multi‑robot path planning methods struggle to scale or generalize to changes, particularly variations in the number of robots during deployment. Most existing methods are trained on a fixed number of robots and may tolerate a reduced number during testing, but typically fail when the number increases. Additionally, training such methods for a larger number of agents can be both time consuming and computationally expensive. However, analytical methods can struggle to scale computationally or handle dynamic changes in the environment. In this work, we propose to leverage a diffusion model based planner capable of handling dynamically varying number of agents. Our approach is trained on a limited number of agents and generalizes effectively to larger numbers of agents during deployment. Results show that integrating a single shared diffusion model based planner with dedicated inter‑agent attention computation and temporal convolution enables a train small deploy‑large paradigm with good accuracy. We validate our method across multiple scenarios and compare the performance with existing multi‑agent reinforcement learning techniques and heuristic control based methods.

Abstract:
Multi‑robot systems (MRS) are essential for large‑scale applications such as disaster response, material transport, and warehouse logistics, yet ensuring robust, safety‑aware formation control in cluttered and dynamic environments remains a major challenge. Existing model predictive control (MPC) approaches suffer from limitations in scalability and provable safety, while control barrier functions (CBFs), though principled for safety enforcement, are difficult to handcraft for large‑scale nonlinear systems. This paper presents FORMULA, a safe distributed, learning‑enhanced predictive control framework that integrates MPC with Control Lyapunov Functions (CLFs) for stability and neural network‑based CBFs for decentralized safety, eliminating manual safety constraint design. This scheme maintains formation integrity during obstacle avoidance, resolves deadlocks in dense configurations, and reduces online computational load. Simulation results demonstrate that FORMULA enables scalable, safety‑aware, formation‑preserving navigation for multi‑robot teams in complex environments.

Abstract:
A key challenge in disaster response is maintaining situational awareness of an evolving landscape, which requires balancing exploration of unobserved regions with sustained monitoring of changing Regions of Interest (ROIs). Unmanned Aerial Vehicles (UAVs) have emerged as an effective response tool, particularly in applications like environmental monitoring and search‑and‑rescue, due to their ability to provide aerial coverage, withstand hazardous conditions, and navigate quickly and flexibly. However, efficient and adaptable multi‑robot coverage with limited sensing in disaster settings and evolving time‑varying information maps remains a significant challenge, necessitating better methods for UAVs to continuously adapt their trajectories in response to changes. In this paper, we propose a decentralized multi‑agent coverage framework that serves as a high‑level planning strategy for adaptive coverage in unknown, time‑varying environments under partial observability. Each agent computes an adaptive ergodic policy, implemented via a Markov‑chain transition model, that tracks a continuously updated belief over the underlying importance map. Gaussian Processes are used to perform those online belief updates. The resulting policy drives agents to spend time in ROIs proportional to their estimated importance, while preserving sufficient exploration to detect and adapt to time‑varying environmental changes. Unlike existing approaches that assume known importance maps, require centralized coordination, or assume a static environment, our framework addresses the combined challenges of unknown, time‑varying distributions in a more realistic decentralized and partially observable setting. We compare against alternative coverage strategies and analyze our method's response to simulated disaster evolution, highlighting its improved adaptability and transient performance in dynamic scenarios.

Abstract:
Perception plays a central role in connected and autonomous vehicles (CAVs), underpinning not only conventional modular driving stacks, but also cooperative perception systems and recent end‑to‑end driving models. While deep learning has greatly improved perception performance, its statistical nature makes perfect predictions difficult to attain. Meanwhile, standard training objectives and evaluation benchmarks treat all perception errors equally, even though only a subset is safety‑critical. In this paper, we investigate safety‑aligned evaluation and optimization for 3D object detection that explicitly characterize high‑impact errors. Building on our previously proposed safety‑oriented metric, NDS‑USC, and safety‑aware loss function, EC‑IoU, we make three contributions. First, we present an expanded study of single‑vehicle 3D object detection models across diverse neural network architectures and sensing modalities, showing that gains under standard metrics such as mAP and NDS may not translate to safety‑oriented criteria represented by NDS‑USC. With EC‑IoU, we reaffirm the benefit of safety‑aware fine‑tuning for improving safety‑critical detection performance. Second, we conduct an ego‑centric, safety‑oriented evaluation of AV‑infrastructure cooperative object detection models, underscoring its superiority over vehicle‑only models and demonstrating a safety impact analysis that illustrates the potential contribution of cooperative models to "Vision Zero." Third, we integrate EC‑IoU into SparseDrive and show that safety‑aware perception hardening can reduce collision rate by nearly 30% and improve system‑level safety directly in an end‑to‑end perception‑to‑planning framework. Overall, our results indicate that safety‑aligned perception evaluation and optimization offer a practical path toward enhancing CAV safety across single‑vehicle, cooperative, and end‑to‑end autonomy settings.

Abstract:
Realizing relative localization by leveraging inter‑robot local measurements is a challenging problem, especially in the presence of measurement noise. Motivated by this challenge, in this paper we propose a novel and systematic 3‑D relative localization framework based on inter‑robot interior angle and self‑displacement measurements. Initially, we propose a linear relative localization theory comprising a distributed linear relative localization algorithm and sufficient conditions for localizability. According to this theory, robots can determine their neighbors' relative positions and orientations in a purely linear manner. Subsequently, in order to deal with measurement noise, we present an advanced Maximum a Posterior (MAP) estimator by addressing three primary challenges existing in the MAP estimator. Firstly, it is common to formulate the MAP problem as an optimization problem, whose inherent non‑convexity can result in local optima. To address this issue, we reformulate the linear computation process of the linear relative localization algorithm as a Weighted Total Least Squares (WTLS) optimization problem on manifolds. The optimal solution of the WTLS problem is more accurate, which can then be used as initial values when solving the optimization problem associated with the MAP problem, thereby reducing the risk of falling into local optima. The second challenge is the lack of knowledge of the prior probability density of the robots' relative positions and orientations at the initial time, which is required as an input for the MAP estimator. To deal with it, we combine the WTLS with a Neural Density Estimator (NDE). Thirdly, to prevent the increasing size of the relative positions and orientations to be estimated as the robots continuously move when solving the MAP problem, a marginalization mechanism is designed, which ensures that the computational cost remains constant.

Abstract:
This paper introduces a new hybrid framework that combines Reinforcement Learning (RL) and Large Language Models (LLMs) to improve robotic manipulation tasks. By utilizing RL for accurate low‑level control and LLMs for high level task planning and understanding of natural language, the proposed framework effectively connects low‑level execution with high‑level reasoning in robotic systems. This integration allows robots to understand and carry out complex, human‑like instructions while adapting to changing environments in real time. The framework is tested in a PyBullet‑based simulation environment using the Franka Emika Panda robotic arm, with various manipulation scenarios as benchmarks. The results show a 33.5% decrease in task completion time and enhancements of 18.1% and 36.4% in accuracy and adaptability, respectively, when compared to systems that use only RL. These results underscore the potential of LLM‑enhanced robotic systems for practical applications, making them more efficient, adaptable, and capable of interacting with humans. Future research will aim to explore sim‑to‑real transfer, scalability, and multi‑robot systems to further broaden the framework's applicability.

Abstract:
Combining different types of agents in uncrewed vehicle (UV) swarms has emerged as an approach to enhance mission resilience and operational capabilities across a wide range of applications. This study offers a systematic framework for grouping different types of swarms based on three main factors: agent nature (behavior and function), hardware structure (physical configuration and sensing capabilities), and operational space (domain of operation). A literature review indicates that strategic heterogeneity significantly improves swarm performance. Operational challenges, including communication architecture constraints, energy‑aware coordination strategies, and control system integration, are also discussed. The analysis shows that heterogeneous swarms are more resilient because they can leverage diverse capabilities, adapt roles on the fly, and integrate data from multidimensional sensor feeds. Some important factors to consider when implementing are sim‑to‑real‑world transfer for learned policies, standardized evaluation metrics, and control architectures that can work together. Learning‑based coordination, GPS (Global Positioning System)‑denied multi‑robot SLAM (Simultaneous Localization and Mapping), and domain‑specific commercial deployments collectively demonstrate that heterogeneous swarm technology is moving closer to readiness for high‑value applications. This study offers a single taxonomy and evidence‑based observations on methods for designing mission‑ready heterogeneous swarms that balance complexity and increased capability.

Abstract:
Distributed multi‑robot systems for the home often require robots to operate out of the user's sight, creating a state awareness gap that can diminish trust and perceived transparency and control. This paper investigates whether real‑time, socially mediated state externalization can bridge this gap without compromising task performance. We developed a system where a co‑located social mediator robot (Pepper) externalizes the hidden execution states of an out‑of‑sight mobile manipulator (Stretch~3) for voice‑driven object retrieval and delivery, where task‑level states are synchronized and externalized through verbal updates and visual progress display. In a counterbalanced within‑subject study (N=30), we compared a baseline of Autonomous Hidden Execution against Socially Mediated State Externalization. Our results show that externalization significantly increases user task‑focused attention (from 15.8% to 84.6%, p<.001) and substantially improves perceived perspicuity, dependability, stimulation, and attractiveness (all p<.001). Furthermore, 83% of participants preferred the externalized condition, and this improvement in user experience was achieved without a statistically significant increase in end‑to‑end task completion time (p=.271). The results suggest that socially mediated state externalization is an effective architectural mechanism for designing more transparent and trustworthy distributed robot systems, ultimately enhancing user experience without sacrificing performance in distributed home robot deployments.

Abstract:
The application of zero‑knowledge proofs (ZKPs) in autonomous systems is an emerging area of research, motivated by the growing need for regulatory compliance, transparent auditing, and trustworthy operation in decentralized environments. zk‑SNARK is a powerful cryptographic tool that allows a party (the prover) to prove to another party (the verifier) that a statement about its own internal state is true, without revealing sensitive or proprietary data about that state. This paper proposes Hermes Seal: a zk‑SNARK‑based ZKP framework for enabling privacy‑preserving, verifiable communication in vehicle‑to‑vehicle (V2V) and vehicle‑to‑infrastructure (V2I) networks. The framework allows autonomous systems to generate cryptographic proofs of perception and decision‑related computations without revealing proprietary models, sensor data, or internal system states, thereby supporting interoperability across heterogeneous autonomous systems. We present two real‑world case studies implemented and empirically evaluated within our framework, demonstrating a step toward verifiable autonomous system information exchanges. The first demonstrates real‑time proof generation and verification, achieving 8 ms proof generation and 1 ms verification on a GPU, while the second evaluates the performance of an autonomous vehicle perception stack, enabling proof of computation without exposing proprietary or confidential data. Furthermore, the framework can be integrated into AV perception stacks to facilitate verifiable interoperability and privacy‑preserving cooperative perception. The demonstration code for this project is open source, available on Github.

Abstract:
Multi‑robot systems can be extremely efficient for accomplishing team‑wise tasks by acting concurrently and collaboratively. However, most existing methods either assume static task features or simply replan when environmental changes occur. This paper addresses the challenging problem of coordinating multi‑robot systems for collaborative tasks involving dynamic and moving targets. We explicitly model the uncertainty in target motion prediction via Conformal Prediction(CP), while respecting the spatial‑temporal constraints specified by Linear Temporal Logic (LTL). The proposed framework (UMBRELLA) combines the Monte Carlo Tree Search (MCTS) over partial plans with uncertainty‑aware rollouts, and introduces a CP‑based metric to guide and accelerate the search. The objective is to minimize the Conditional Value at Risk (CVaR) of the average makespan. For tasks released online, a receding‑horizon planning scheme dynamically adjusts the assignments based on updated task specifications and motion predictions. Spatial and temporal constraints among the tasks are always ensured, and only partial synchronization is required for the collaborative tasks during online execution. Extensive large‑scale simulations and hardware experiments demonstrate substantial reductions in both the average makespan and its variance by 23% and 71%, compared with static baselines.

Abstract:
Accurate forecasting of future solar irradiance is essential for the effective control of solar thermal power plants. Although various kriging‑based methods have been proposed to address the prediction problem, these methods typically do not provide an appropriate sampling strategy to dynamically position mobile sensors for optimizing prediction accuracy in real time, which is critical for achieving accurate forecasts with a minimal number of sensors. This paper introduces a dissimilarity map derived from a kriging model and proposes a persistent coverage control algorithm that effectively guides agents toward regions where additional observations are required to improve prediction performance. By means of experiments using mobile robots, the proposed approach was shown to obtain more accurate predictions than the considered baselines under various emulated irradiance fields.

Abstract:
Drone light shows (DLShows) represent a rapidly growing application of swarm robotics, creating captivating aerial displays through the synchronized flight of hundreds or thousands of unmanned aerial vehicles (UAVs) as environmentally friendly and reusable alternatives to traditional pyrotechnics. This domain presents unique challenges in optimally assigning drones to visual waypoints and generating smooth, collision‑free trajectories at a very large scale. This article introduces the Unified Assignment and Trajectory Generation (UATG) framework. The proposed approach concurrently solves two core problems: the optimal assignment of drones to designated goal locations and the generation of dynamically feasible, collision‑free, time‑parameterized trajectories. The UATG framework is specifically designed for DLShows, ensuring minimal transition times between formations and guaranteeing inter‑drone collision avoidance. A key innovation is its exceptional computational efficiency, enabling the coordination of large‑scale in real‑time; for instance, it computes the optimal assignment and trajectories for 1008 drones in approximately one second on a standard laptop. Extensive simulations in realistic environments validate the framework's performance, demonstrating its capability to orchestrate complex formations, from alphanumeric characters to intricate 3D shapes, with precision and visual smoothness. This work provides a critical advancement for the DLShow industry, offering a practical and scalable solution for generating complex aerial choreography and establishing a valuable benchmark for ground control station software designed for the efficient coordination of multiple UAVs. A supplemental animated simulation of this work is available at https://youtu.be/‑Fjrhw03594.

Abstract:
Achieving agile and reconfigurable production flows in smart factories depends on online multi‑robot task assignment (MRTA), which requires online collision‑free and congestion‑free route scheduling of transportation multi‑robot systems (T‑MRS), e.g., collaborative automatic guided vehicles (AGVs). Due to the real‑time operational requirements and dynamic interactions between T‑MRS and production MRS, online scheduling under partial observability in dynamic factory environments remains a significant and under‑explored challenge. This paper proposes a novel communication‑enabled online scheduling framework that explicitly couples wireless machine‑to‑machine (M2M) networking with route scheduling, enabling AGVs to exchange intention information, e.g., planned routes, to overcome partial observations and assist complex computation of online scheduling. Specifically, we determine intelligent AGVs' intention and sensor data as new M2M traffic and tailor the retransmission‑free multi‑link transmission networking to meet real‑time operation demands. This scheduling‑oriented networking is then integrated with a simulated annealing‑based MRTA scheme and a congestion‑aware A‑based route scheduling method. The integrated communication and scheduling scheme allows AGVs to dynamically adjust collision‑free and congestion‑free routes with reduced computational overhead. Numerical experiments shows the impacts from wireless communication on the performance of T‑MRS and suggest that the proposed integrated scheme significantly enhances scheduling efficiency compared to other baselines, even under high AGV load conditions and limited channel resources. Moreover, the results reveal that the scheduling‑oriented wireless M2M communication design fundamentally differs from human‑to‑human communications, implying new technological opportunities in a wireless networked smart factory.

Abstract:
Collaboration is a central theme in multi‑robot systems as tasks and demands increasingly require capabilities that go beyond what any one individual robot possesses. Yet, despite extensive work on cooperative control and coordinated behaviors, the terminology surrounding collective multi‑robot interaction remains inconsistent across research communities. In particular, cooperation, coordination, and collaboration are often treated interchangeably, without clearly articulating the differences among them. To address this gap, we propose definitions that distinguish and relate cooperation, coordination, and collaboration in multi‑robot systems, highlighting the support of new capabilities in collaborative behaviors, and illustrate these concepts through representative examples. Building on this taxonomy, different frameworks for collaboration are reviewed, and technical challenges and promising future research directions are identified for collaborative multi‑robot systems.

Abstract:
Modern manufacturing under High‑Mix‑Low‑Volume requirements increasingly relies on flexible and adaptive matrix production systems, which depend on interconnected heterogeneous devices and rapid task reconfiguration. To address these needs, we present ROSCell, a ROS2‑based framework that enables the flexible formation and management of a computing continuum across various devices. ROSCell allows users to package existing robotic software as deployable skills and, with simple requests, assemble isolated cells, automatically deploy skill instances, and coordinate their communication to meet task objectives. It provides a scalable and low‑overhead foundation for adaptive multi‑robot computing in dynamic production environments. Experimental results show that, in the idle state, ROSCell substantially reduces CPU, memory, and network overhead compared to K3s‑based solutions on edge devices, highlighting its energy efficiency and cost‑effectiveness for large‑scale deployment in production settings. The source code, examples, and documentation will be provided on Github.

Abstract:
This paper presents a hierarchical two‑stage framework for multi‑robot task allocation and trajectory optimization in asymmetric task spaces: (1) a sequential auction allocates tasks using closed‑form bid functions, and (2) each robot independently solves an optimal control problem for energy‑minimal trajectories with a physics‑based battery model, followed by a collision avoidance refinement step using pairwise proximity penalties. Event‑triggered warm‑start rescheduling with bounded trigger frequency handles robot faults, priority arrivals, and energy deviations. Across 505 scenarios with 2‑20 robots and up to 100 tasks on three factory layouts, both energy‑ and distance‑based auction variants achieve 11.8% average energy savings over nearest‑task allocation, with rescheduling latency under 10 ms. The central finding is that bid‑metric performance is regime‑dependent: in uniform workspaces, distance bids outperform energy bids by 3.5% (p < 0.05, Wilcoxon) because a 15.7% closed‑form approximation error degrades bid ranking accuracy to 87%; however, when workspace friction heterogeneity is sufficient (r < 0.85 energy‑distance correlation), a zone‑aware energy bid outperforms distance bids by 2‑2.4%. These results provide practitioner guidance: use distance bids in near‑uniform terrain and energy‑aware bids when friction variation is significant.

Abstract:
Automating large‑scale manufacturing in domains like timber construction requires multi‑robot systems to manage tightly coupled spatiotemporal constraints, such as collision avoidance and process‑driven deadlines. This paper introduces LASER (Level‑based Asynchronous Scheduling and Execution Regime), a complete framework for scheduling and executing complex assembly tasks, demonstrated on a screw‑press gluing application for timber slab manufacturing. Our central contribution is to integrate a barrier‑based mechanism into a constraint programming (CP) scheduling formulation that partitions tasks into spatiotemporally disjoint sets, which we define as levels. This structure enables robots to execute tasks in parallel and asynchronously within a level, synchronizing only at level barriers, which guarantees collision‑free operation by construction and provides robustness to timing uncertainties. To solve this formulation for large problems, we propose two specialized algorithms: an iterative temporal‑relaxation approach for heterogeneous task sequences and a bi‑level decomposition for homogeneous tasks that balances workload. We validate the LASER framework by fabricating a full‑scale 2.4m x 6m timber slab with a two‑robot system mounted on parallel linear tracks, successfully coordinating 108 subroutines and 352 screws under tight adhesive time windows. Computational studies show our method scales steadily with size compared to a monolithic approach.

Abstract:
Cooperative perception among autonomous agents overcomes the limitations of single‑agent sensing, but bandwidth constraints in vehicle‑to‑everything (V2X) networks require efficient communication policies. Existing approaches rely on reactive mechanisms, such as confidence maps, learned gating, or sparse masks, to decide what to transmit, without reasoning about why a message benefits the receiver. We introduce Reason‑to‑Transmit (R2T), a framework that equips each agent with a lightweight transformer‑based module that reasons over local scene context, estimated neighbor information gaps, and bandwidth budget to make per‑region transmission decisions. Trained end‑to‑end with a bandwidth‑aware objective, R2T is evaluated against nine baselines in a multi‑agent bird's‑eye‑view perception environment. Any communication improves performance by about 58% AP over no communication. At low bandwidth, all selective methods perform similarly, but R2T shows clear gains under high occlusion, where information asymmetry is greatest, approaching oracle performance. All methods degrade gracefully under packet drops up to 50%, showing robustness to communication failures. These results indicate that while fusion design dominates performance, deliberative communication provides additional gains in challenging scenarios. R2T introduces a reasoning‑based approach to communication, enabling more efficient and context‑aware information sharing in cooperative perception.

Abstract:
Cooperative multi‑agent methods for embodied AI are almost universally evaluated under idealized communication: zero latency, no packet loss, and unlimited bandwidth. Real‑world deployment on robots with wireless links, autonomous vehicles on congested networks, or drone swarms in contested spectrum offers no such guarantees. We introduce AgentComm‑Bench, a benchmark suite and evaluation protocol that systematically stress‑tests cooperative embodied AI under six communication impairment dimensions: latency, packet loss, bandwidth collapse, asynchronous updates, stale memory, and conflicting sensor evidence. AgentComm‑Bench spans three task families: cooperative perception, multi‑agent waypoint navigation, and cooperative zone search, and evaluates five communication strategies, including a lightweight method we propose based on redundant message coding with staleness‑aware fusion. Our experiments reveal that communication‑dependent tasks degrade catastrophically: stale memory and bandwidth collapse cause over 96% performance drops in navigation, while content corruption (stale or conflicting data) reduces perception F1 by over 85%. Vulnerability depends on the interaction between impairment type and task design; perception fusion is robust to packet loss but amplifies corrupted data. Redundant message coding more than doubles navigation performance under 80% packet loss. We release AgentComm‑Bench as a practical evaluation protocol and recommend that cooperative embodied AI work report performance under multiple impairment conditions.

Abstract:
In multi‑robot systems, traditional radio frequency (RF) communication struggles with contention and jamming. Optical communication offers a strong alternative. However, conventional frame‑based cameras suffer from limited frame rates, motion blur, and reduced robustness under high dynamic range lighting. Event cameras support microsecond temporal resolution and high dynamic range, making them extremely sensitive to scene changes under fast relative motion with an optical transmitter. Leveraging these strengths, we develop a complete optical communication system capable of tracking moving transmitters and decoding messages in real time. Our system achieves over 95% decoding accuracy for text transmission during motion by implementing a Geometry‑Aware Unscented Kalman Filter (GA‑UKF), achieving 7x faster processing speed compared to the previous state‑of‑the‑art method, while maintaining equivalent tracking accuracy at transmitting frequencies \geq 1 kHz.

Abstract:
Applying micro‑patterns to surfaces has been shown to impart useful physical properties such as drag reduction and hydrophobicity. However, current manufacturing techniques cannot produce micro‑patterned surfaces at scale due to high‑cost machinery and inefficient coverage techniques such as raster‑scanning. In this work, we use multiple robots, each equipped with a patterning tool, to manufacture these surfaces. To allow these robots to coordinate during the patterning task, we use the ergodic control algorithm, which specifies coverage objectives using distributions. We demonstrate that robots can divide complicated coverage objectives by communicating compressed representations of their trajectory history both in simulations and experimental trials. Further, we show that robot‑produced patterning can lower the coefficient of friction of metallic surfaces. This work demonstrates that distributed multi‑robot systems can coordinate to manufacture products that were previously unrealizable at scale.

Abstract:
Communication is a core enabler for multi‑robot systems (MRS), providing the mechanism through which robots exchange state information, coordinate actions, and satisfy safety constraints. While many MRS autonomy algorithms assume reliable and timely message delivery, realistic wireless channels introduce delay, erasures, and ordering stalls that can degrade performance and compromise safety‑critical decisions of the robot task. In this paper, we investigate how transport‑layer reliability mechanisms that mitigate communication losses and delays shape the autonomy‑communication loop. We show that conventional non‑coded retransmission‑based protocols introduce long delays that are misaligned with the timeliness requirements of MRS applications, and may render the received data irrelevant. As an alternative, we advocate for adaptive and causal network coding, which proactively injects coded redundancy to achieve the desired delay and throughput that enable relevant data delivery to the robotic task. Specifically, this method adapts to channel conditions between robots and causally tunes the communication rates via efficient algorithms. We present two case studies: cooperative localization under delayed and lossy inter‑robot communication, and a safety‑critical overtaking maneuver where timely vehicle‑to‑vehicle message availability determines whether an ego vehicle can abort to avoid a crash. Our results demonstrate that coding‑based communication significantly reduces in‑order delivery stalls, preserves estimation consistency under delay, and improves deadline reliability relative to retransmission‑based transport. Overall, the study highlights the need to jointly design autonomy algorithms and communication mechanisms, and positions network coding as a principled tool for dependable multi‑robot operation over wireless networks.

Abstract:
Multi‑uncrewed aerial vehicle (UAV) cooperative perception has emerged as a promising paradigm for diverse low‑altitude economy applications, where complementary multi‑view observations are leveraged to enhance perception performance via wireless communications. However, the massive visual data generated by multiple UAVs poses significant challenges in terms of communication latency and resource efficiency. To address these challenges, this paper proposes a communication‑efficient cooperative perception framework, termed Base‑Station‑Helped UAV (BHU), which reduces communication overhead while enhancing perception performance. Specifically, we employ a Top‑K selection mechanism to identify the most informative pixels from UAV‑captured RGB images, enabling sparsified visual transmission with reduced data volume and latency. The sparsified images are transmitted to a ground server via multi‑user MIMO (MU‑MIMO), where a Swin‑large‑based MaskDINO encoder extracts bird's‑eye‑view (BEV) features and performs cooperative feature fusion for ground vehicle perception. Furthermore, we develop a diffusion model‑based deep reinforcement learning (DRL) algorithm to jointly select cooperative UAVs, sparsification ratios, and precoding matrices, achieving a balance between communication efficiency and perception utility. Simulation results on the Air‑Co‑Pred dataset demonstrate that, compared with traditional CNN‑based BEV fusion baselines, the proposed BHU framework improves perception performance by over 5% while reducing communication overhead by 85%, providing an effective solution for multi‑UAV cooperative perception under resource‑constrained wireless environments.

Abstract:
Robots are increasingly deployed across diverse domains and designed for multi‑purpose operation. As robotic systems grow in complexity and operate in dynamic environments, the need for structured, expressive, and scalable mission‑specification approaches becomes critical, with mission specifications often defined in the field by domain experts rather than robotics specialists. However, there is no standard or widely accepted formalism for specifying missions in single‑ or multi‑robot systems. A variety of formalisms, such as Behavior Trees, State Machines, Hierarchical Task Networks, and Business Process Model and Notation, have been adopted in robotics to varying degrees, each providing different levels of abstraction, expressiveness, and support for integration with human workflows and external devices. This paper presents a systematic analysis of these four formalisms with respect to their suitability for robot mission specification. Our study focuses on mission‑level descriptions rather than robot software development. We analyze their underlying control structures and mission concepts, evaluate their expressiveness and limitations in modeling real‑world missions, and assess the extent of available tool support. By comparing the formalisms and validating our findings with experts, we provide insights into their applicability, strengths, and shortcomings in robotic system modeling. The results aim to support practitioners and researchers in selecting appropriate modeling approaches for designing robust and adaptable robot and multi‑robot missions.

Abstract:
Multi‑agent reinforcement learning (MARL) commonly relies on a centralized critic to estimate the value function. However, learning such a critic from scratch is highly sample‑inefficient and often lacks generalization across environments. At the same time, large vision‑language‑action models (VLAs) trained on internet‑scale data exhibit strong multimodal reasoning and zero‑shot generalization capabilities, yet directly deploying them for robotic execution remains computationally prohibitive, particularly in heterogeneous multi‑robot systems with diverse embodiments and resource constraints. To address these challenges, we propose Multi‑Agent Vision‑Language‑Critic Models (MA‑VLCM), a framework that replaces the learned centralized critic in MARL with a pretrained vision‑language model fine‑tuned to evaluate multi‑agent behavior. MA‑VLCM acts as a centralized critic conditioned on natural language task descriptions, visual trajectory observations, and structured multi‑agent state information. By eliminating critic learning during policy optimization, our approach significantly improves sample efficiency while producing compact execution policies suitable for deployment on resource‑constrained robots. Results show good zero‑shot return estimation on models with differing VLM backbones on in‑distribution and out‑of‑distribution scenarios in multi‑agent team settings

Abstract:
Multi‑robot systems are increasingly deployed in high‑risk missions such as reconnaissance, disaster response, and subterranean operations. Protecting a human operator while navigating unknown and adversarial environments remains a critical challenge, especially when the communication among the operator and robots is restricted. Unlike existing collaborative exploration methods that aim for complete coverage, this work focuses on task‑oriented exploration to minimize the navigation time of the operator to reach its goal while ensuring safety under adversarial threats. A novel escorting framework BodyGuards, is proposed to explicitly integrate seamlessly collaborative exploration, inter‑robot‑operator communication and escorting. The framework consists of three core components: (I) a dynamic movement strategy for the operator that maintains a local map with risk zones for proactive path planning; (II) a dual‑mode robotic strategy combining frontier based exploration with optimized return events to balance exploration, threat detection, and intermittent communication; and (III) multi‑robot coordination protocols that jointly plan exploration and information sharing for efficient escorting. Extensive human‑in‑the‑loop simulations and hardware experiments demonstrate that the method significantly reduces operator risk and mission time, outperforming baselines in adversarial and constrained environments.

Abstract:
We present a progress‑based fault detection module and its integration with dynamic task allocation for heterogeneous robot teams. The detector monitors a normalized task‑completion signal with a lightweight Kalman filter (KF) and a normalized innovation squared (NIS) test, augmented with a low‑rate stall gate, an uncertainty gate, and debounce logic. Health estimates influence the allocator via health‑weighted costs and health‑dependent masks; reallocation is event‑triggered and regularized with an \ell_1 assignment‑change penalty to limit reassignment churn while preserving feasibility through slack variables. The detector has constant per‑robot update cost, and the allocation remains a convex quadratic program (QP). Experiments on a common team‑task setup evaluate measurement‑noise increases, velocity‑slip biases, communication dropouts, and task abandonment. The results show timely detection in the noise and bias cases, maintained task completion with limited reassignment, and the expected observability delays under communication dropouts.

Abstract:
Energy‑aware algorithms for multi‑robot systems require accurate power consumption models, yet existing approaches rely on kinematic approximations that fail to capture the complex dynamics of real hardware. We present a lightweight autoregressive predictor for the GTernal mobile robot platform deployed in the Georgia Tech Robotarium. Through analysis of 48,000 samples collected across six motion trials, we discover that power consumption exhibits strong temporal autocorrelation (ρ_1 = 0.95) that dominates kinematic effects. A 7,041‑parameter multi‑layer perceptron (MLP) achieves R^2 = 0.90 on held‑out motion patterns by conditioning on recent power history, reaching the theoretical prediction ceiling imposed by measurement noise. Physical validation across seven robots in a collision avoidance scenario yields mean R^2 = 0.87, demonstrating zero‑shot transfer to unseen robots and behaviors. The predictor runs in 224 μs per inference, enabling real‑time deployment at 150× the platform's 30 Hz control rate. We release the trained model and dataset to support energy‑aware multi‑robot algorithm development.

Abstract:
Real‑world robots often operate in settings where objective priorities depend on the underlying context of operation. When the underlying context is unknown apriori, multiple robots may have to coordinate to gather informative observations to infer the context, since acting based on an incorrect context can lead to misaligned and unsafe behavior. Once the underlying true context is inferred, the robots optimize their task‑specific objectives in the preference order induced by the context. We formalize this problem as a Multi‑Robot Context‑Uncertain Stochastic Shortest Path (MR‑CUSSP), which captures context‑relevant information at landmark states through joint observations. Our two‑stage solution approach is composed of: (1) CIMOP (Coordinated Inference for Multi‑Objective Planning) to compute plans that guide robots toward informative landmarks to efficiently infer the true context, and (2) LCBS (Lexicographic Conflict‑Based Search) for collision‑free multi‑robot path planning with lexicographic objective preferences, induced by the context. We evaluate the algorithms using three simulated domains and demonstrate its practical applicability using five mobile robots in the salp domain setup.

Abstract:
Coordinated operations of multi‑robot systems (MRS) require agents to maintain communication connections to accomplish team objectives. However, maintaining the connections imposes costs in terms of restricted robot mobility, resulting in suboptimal team performance. In this work, we consider a realistic MRS framework in which agents are subject to unknown dynamical disturbances and experience communication delays. Most existing works on connectivity maintenance use consensus‑based frameworks for graph reconfiguration, where decision‑making time scales with the number of nodes and requires multiple rounds of communication, making them ineffective under communication delays. To address this, we propose a novel leader‑based decision‑making algorithm that uses a central node for efficient real‑time reconfiguration, reducing decision‑making time to depend on the graph diameter rather than the number of nodes and requiring only one round of information transfer through the network. We propose a novel method for estimating robot locations within the MRS that actively accounts for unknown disturbances and the communication delays. Using these position estimates, the central node selects a set of edges to delete while allowing the formation of new edges, aiming to keep the diameter of the new graph within a threshold. We provide numerous simulation results to showcase the efficacy of the proposed method.

Abstract:
Agricultural environments present high proportions of spatially dense navigation bottlenecks for long‑term navigation and operational planning of agricultural mobile robots. The existing agent‑centric multi‑robot path planning (MRPP) approaches resolve conflicts from the perspective of agents, rather than from the resources under contention. Further, the density of such contentions limits the capabilities of spatial interleaving, a concept that many planners rely on to achieve high throughput. In this work, two variants of the priority‑based Fragment Planner (FP) are presented as resource‑centric MRPP algorithms that leverage route fragmentation to enable partial route progression and limit the impact of binary‑based waiting. These approaches are evaluated in lifelong simulation over a 3.6km topological map representing a commercial polytunnel environment. Their performances are contrasted against 5 baseline algorithms with varying robotic fleet sizes. The Fragment Planners achieved significant gains in throughput compared with Prioritised Planning (PP) and Priority‑Based Search (PBS) algorithms. They further demonstrated a task throughput of 95% of the optimal task throughput over the same time period. This work shows that, for long‑term deployment of agricultural robots in corridor‑dominant agricultural environments, resource‑centric MRPP approaches are a necessity for high‑efficacy operational planning.

Abstract:
Deploying multi‑robot systems in environments shared with dynamic and uncontrollable agents presents significant challenges, especially for large robot fleets. In such environments, individual robot operations can be delayed due to unforeseen conflicts with uncontrollable agents. While existing research primarily focuses on preserving the completeness of Multi‑Agent Path Finding (MAPF) solutions considering delays, there is limited emphasis on utilizing additional environmental information to enhance solution quality in the presence of other dynamic agents. To this end, we propose Flow‑Aware Multi‑Agent Path Finding (FA‑MAPF), a novel framework that integrates learned motion patterns of uncontrollable agents into centralized MAPF algorithms. Our evaluation, conducted on a diverse set of benchmark maps with simulated uncontrollable agents and on a real‑world map with recorded human trajectories, demonstrates the effectiveness of FA‑MAPF compared to state‑of‑the‑art baselines. The experimental results show that FA‑MAPF can consistently reduce conflicts with uncontrollable agents, up to 55%, without compromising task efficiency.

Abstract:
Decentralized cooperative localization (DCL) is a promising approach for nonholonomic mobile robots operating in GPS‑denied environments with limited communication infrastructure. This paper presents a DCL framework in which each robot performs localization locally using an Extended Kalman Filter, while sharing measurement information during update stages only when communication links are available and companion robots are successfully detected by LiDAR. The framework preserves cross‑correlation consistency among robot state estimates while handling asynchronous sensor data with heterogeneous sampling rates and accommodating accelerations during dynamic maneuvers. Unlike methods that require pre‑aligned coordinate systems, the proposed approach allows robots to initialize with arbitrary reference‑frame orientations and achieves automatic alignment through transformation matrices in both the prediction and update stages. To improve robustness in feature‑sparse environments, we introduce a dual‑landmark evaluation framework that exploits both static environmental features and mobile robots as dynamic landmarks. The proposed framework enables reliable detection and feature extraction during sharp turns, while prediction accuracy is improved through information sharing from mutual observations. Experimental results in both Gazebo simulation and real‑world basement environments show that DCL outperforms centralized cooperative localization (CCL), achieving a 34% reduction in RMSE, while the dual‑landmark variant yields an improvement of 56%. These results demonstrate the applicability of DCL to challenging domains such as enclosed spaces, underwater environments, and feature‑sparse terrains where conventional localization methods are ineffective.

Abstract:
The integration of cloud computing and edge computing is an effective way to achieve global consistent and real‑time multi‑robot Simultaneous Localization and Mapping (SLAM). Cloud computing effectively solves the problem of limited computing, communication and storage capacity of terminal equipment. However, limited bandwidth and extremely long communication links between terminal devices and the cloud result in serious performance degradation of multi‑robot SLAM systems. To reduce the computational cost of feature tracking and improve the real‑time performance of the robot, a lightweight SLAM method of optical flow tracking based on pyramid IMU prediction is proposed. On this basis, a centralized multi‑robot SLAM system based on a robot‑edge‑cloud layered architecture is proposed to realize real‑time collaborative SLAM. It avoids the problems of limited on‑board computing resources and low execution efficiency of single robot. In this framework, only the feature points and keyframe descriptors are transmitted and lossless encoding and compression are carried out to realize real‑time remote information transmission with limited bandwidth resources. This design reduces the actual bandwidth occupied in the process of data transmission, and does not cause the loss of SLAM accuracy caused by data compression. Through experimental verification on the EuRoC dataset, compared with the current most advanced local feature compression method, our method can achieve lower data volume feature transmission, and compared with the current advanced centralized multi‑robot SLAM scheme, it can achieve the same or better positioning accuracy under low computational load.

Abstract:
Advancing Multi‑Agent Pathfinding (MAPF) and Multi‑Robot Motion Planning (MRMP) requires platforms that enable transparent, reproducible comparisons across modeling choices. Existing tools either scale under simplifying assumptions (grids, homogeneous agents) or offer higher fidelity with less comparable instrumentation. We present GRACE, a unified 2D simulator+benchmark that instantiates the same task at multiple abstraction levels (grid, roadmap, continuous) via explicit, reproducible operators and a common evaluation protocol. Our empirical results on public maps and representative planners enable commensurate comparisons on a shared instance set. Furthermore, we quantify the expected representation‑fidelity trade‑offs (MRMP solves instances at higher fidelity but lower speed, while grid/roadmap planners scale farther). By consolidating representation, execution, and evaluation, GRACE thereby aims to make cross‑representation studies more comparable and provides a means to advance multi‑robot planning research and its translation to practice.

Abstract:
Large deep neural networks (DNNs), especially transformer‑based and multimodal architectures, are computationally demanding and challenging to deploy on resource‑constrained edge platforms like field robots. These challenges intensify in mission‑critical scenarios (e.g., disaster response), where robots must collaborate under tight constraints on bandwidth, latency, and battery life, often without infrastructure or server support. To address these limitations, we present COHORT, a collaborative DNN inference and task‑execution framework for multi‑robot systems built on the Robotic Operating System (ROS). COHORT employs a hybrid offline‑online reinforcement learning (RL) strategy to dynamically schedule and distribute DNN module execution across robots. Our key contributions are threefold: (a) Offline RL policy learning combined with Advantage‑Weighted Regression (AWR), trained on auction‑based task allocation data from heterogeneous DNN workloads across distributed robots, (b) Online policy adaptation via Multi‑Agent PPO (MAPPO), initialized from the offline policy and fine‑tuned in real time, and (c) comprehensive evaluation of COHORT on vision‑language model (VLM) inference tasks such as CLIP and SAM, analyzing scalability with increasing robot/workload and robustness under . We benchmark COHORT against genetic algorithms and multiple RL baselines. Experimental results demonstrate that COHORT reduces battery consumption by 15.4% and increases GPU utilization by 51.67%, while satisfying frame‑rate and deadline constraints 2.55 times of the time.

Abstract:
Cooperative localization (CL) enables accurate position estimation in multi‑robot systems operating in GPS‑denied environments. This paper presents a comparative study of five CL approaches: Centralized Cooperative Localization (CCL), Decentralized Cooperative Localization (DCL), Sequential Cooperative Localization (StCL), Covariance Intersection (CI), and Standard Cooperative Localization (Standard‑CL). All methods are implemented in ROS and evaluated through Monte Carlo simulations under two conditions: weak data association and robust detection. Our analysis reveals fundamental trade‑offs among the methods. StCL and Standard‑CL achieve the lowest position errors but exhibit severe filter inconsistency, making them unsuitable for safety‑critical applications. DCL demonstrates remarkable stability under challenging conditions due to its measurement stride mechanism, which provides implicit regularization against outliers. CI emerges as the most balanced approach, achieving near‑optimal consistency while maintaining competitive accuracy. CCL provides theoretically optimal estimation but shows sensitivity to measurement outliers. These findings offer practical guidance for selecting CL algorithms based on application requirements.

Abstract:
To address the challenge of efficient coverage by multi‑robot systems in non‑convex regions with multiple obstacles, this paper proposes a coverage control method based on the Generalized Voronoi Graph (GVG), which has two phases: Load‑Balancing Algorithm phase and Collaborative Coverage phase. In Load‑Balancing Algorithm phase, the non‑convex region is partitioned into multiple sub‑regions based on GVG. Besides, a weighted load‑balancing algorithm is developed, which considers the quality differences among sub‑regions. By iteratively optimizing the robot allocation ratio, the number of robots in each sub‑region is matched with the sub‑region quality to achieve load balance. In Collaborative Coverage phase, each robot is controlled by a new controller to effectively coverage the region. The convergence of the method is proved and its performance is evaluated through simulations.

Abstract:
Task specialization can lead to simpler robot behaviors and higher efficiency in multi‑robot systems. Previous works have shown the emergence of task specialization during evolutionary optimization, focusing on feasibility rather than costs. In this study, we take first steps toward a cost‑benefit analysis of task specialization in robot swarms using a foraging scenario. We evolve artificial neural networks as generalist behaviors for the entire task and as task‑specialist behaviors for subtasks within a limited evaluation budget. We show that generalist behaviors can be successfully optimized while the evolved task‑specialist controllers fail to cooperate efficiently, resulting in worse performance than the generalists. Consequently, task specialization does not necessarily improve efficiency when optimization budget is limited.

Abstract:
Long‑horizon task planning for heterogeneous multi‑robot systems is essential for deploying collaborative teams in real‑world environments; yet, it remains challenging due to the large volume of perceptual information, much of which is irrelevant to task objectives and burdens planning. Traditional symbolic planners rely on manually constructed problem specifications, limiting scalability and adaptability, while recent large language model (LLM)‑based approaches often suffer from hallucinations and weak grounding‑i.e., poor alignment between generated plans and actual environmental objects and constraints‑in object‑rich settings. We present Scale‑Plan, a scalable LLM‑assisted framework that generates compact, task‑relevant problem representations from natural language instructions. Given a PDDL domain specification, Scale‑Plan constructs an action graph capturing domain structure and uses shallow LLM reasoning to guide a structured graph search that identifies a minimal subset of relevant actions and objects. By filtering irrelevant information prior to planning, Scale‑Plan enables efficient decomposition, allocation, and long‑horizon plan generation. We evaluate our approach on complex multi‑agent tasks and introduce MAT2‑THOR, a cleaned benchmark built on AI2‑THOR for reliable evaluation of multi‑robot planning systems. Scale‑Plan outperforms pure LLM and hybrid LLM‑PDDL baselines across all metrics, improving scalability and reliability.

Abstract:
Accurate and safety‑conscious trajectory prediction is a key technology for intelligent transportation systems, especially in V2X‑enabled urban environments with complex multi‑agent interactions. In this paper, we created a digital twin‑driven V2X trajectory prediction pipeline that jointly leverages cooperative perception from vehicles and infrastructure to forecast multi‑agent motion at signalized intersections. The proposed model combines a Bi‑LSTM‑based generator with a structured training objective consisting of a standard mean squared error (MSE) loss and a novel twin loss. The twin loss encodes infrastructure constraints, collision avoidance, diversity across predicted modes, and rule‑based priors derived from the digital twin. While the MSE term ensures point‑wise accuracy, the twin loss penalizes traffic rule violations, predicted collisions, and mode collapse, guiding the model toward scene‑consistent and safety‑compliant predictions. We train and evaluate our approach on real‑world V2X data sent from the intersection to the vehicle and collected in urban corridors. In addition to standard trajectory metrics (ADE, FDE), we introduce ITS‑relevant safety indicators, including infrastructure and rule violation rates. Experimental results demonstrate that the proposed training scheme significantly reduces critical violations while maintaining comparable prediction accuracy and real‑time performance, highlighting the potential of digital twin‑driven multi‑loss learning for V2X‑enabled intelligent transportation systems.

Abstract:
Cooperative perception significantly enhances scene understanding by integrating complementary information from diverse agents. However, existing research often overlooks critical challenges inherent in real‑world multi‑source data integration, specifically high temporal latency and multi‑source noise. To address these practical limitations, we propose Collaborative Alignment and Transformation Network (CATNet), an adaptive compensation framework that resolves temporal latency and noise interference in multi‑agent systems. Our key innovations can be summarized in three aspects. First, we introduce a Spatio‑Temporal Recurrent Synchronization (STSync) that aligns asynchronous feature streams via adjacent‑frame differential modeling, establishing a temporal‑spatially unified representation space. Second, we design a Dual‑Branch Wavelet Enhanced Denoiser (WTDen) that suppresses global noise and reconstructs localized feature distortions within aligned representations. Third, we construct an Adaptive Feature Selector (AdpSel) that dynamically focuses on critical perceptual features for robust fusion. Extensive experiments on multiple datasets demonstrate that CATNet consistently outperforms existing methods under complex traffic conditions, proving its superior robustness and adaptability.

Abstract:
This paper presents a novel approach to multi‑robot collision avoidance that integrates global path planning with local navigation strategies, utilizing attentive graph neural networks to manage dynamic interactions among agents. We introduce a local navigation model that leverages pre‑planned global paths, allowing robots to adhere to optimal routes while dynamically adjusting to environmental changes. The models robustness is enhanced through the introduction of noise during training, resulting in superior performance in complex, dynamic environments. Our approach is evaluated against established baselines, including NH‑ORCA, DRL‑NAV, and GA3C‑CADRL, across various structurally diverse simulated scenarios. The results demonstrate that our model achieves consistently higher success rates, lower collision rates, and more efficient navigation, particularly in challenging scenarios where baseline models struggle. This work offers an advancement in multi‑robot navigation, with implications for robust performance in complex, dynamic environments with varying degrees of complexity, such as those encountered in logistics, where adaptability is essential for accommodating unforeseen obstacles and unpredictable changes.

Abstract:
Recent advances in sensing, communication, interfaces, control, and robotics are expanding Human‑Building Interaction (HBI) beyond adaptive building services and facades toward the physical actuation of architectural space. In parallel, research in robotic furniture, swarm robotics, and shape‑changing spaces shows that architectural elements can now be robotically augmented to move, reconfigure, and adapt space. We propose that these advances promise a paradigm shift in HBI, in which multiple building layers physically adapt in synchrony to support occupant needs and sustainability goals more holistically. Conversely, we argue that this emerging paradigm also provides an ideal case for transferring HRI knowledge to unconventional robotic morphologies, including the interpretation of the robot as multiple architectural layers or even as a building. However, this research agenda remains challenged by the temporal, spatial, and social complexity of architectural HRI, and by fragmented knowledge across HCI, environmental psychology, cognitive science, and architecture. We therefore call for interdisciplinary research that unifies the why, what, and how of robotic actuation in architectural forms.

Abstract:
Efficient communication is critical for decentralized Multi‑Robot Path Planning (MRPP), yet existing learned communication methods treat all neighboring robots equally regardless of their spatial proximity, leading to diluted attention in congested regions where coordination matters most. We propose Relation enhanced Multi Head Attention (RMHA), a communication mechanism that explicitly embeds pairwise Manhattan distances into the attention weight computation, enabling each robot to dynamically prioritize messages from spatially relevant neighbors. Combined with a distance‑constrained attention mask and GRU gated message fusion, RMHA integrates seamlessly with MAPPO for stable end‑to‑end training. In zero‑shot generalization from 8 training robots to 128 test robots on 40x40 grids, RMHA achieves approximately 75 percent success rate at 30 percent obstacle density outperforming the best baseline by over 25 percentage points. Ablation studies confirm that distance‑relation encoding is the key contributor to success rate improvement in high‑density environments. Index Terms‑Multi‑robot path planning, graph attention mechanism, multi‑head attention, communication optimization, cooperative decision‑making

Abstract:
In imitation learning, robots are supposed to learn from demonstrations of the desired behavior. Most of the work in imitation learning for swarm robotics provides the demonstrations as rollouts of an existing policy. In this work, we provide a framework based on generative adversarial imitation learning that aims to learn collective behaviors from human demonstrations. Our framework is evaluated across six different missions, learning both from manual demonstrations and demonstrations derived from a PPO‑trained policy. Results show that the imitation learning process is able to learn qualitatively meaningful behaviors that perform similarly well as the provided demonstrations. Additionally, we deploy the learned policies on a swarm of TurtleBot 4 robots in real‑robot experiments. The exhibited behaviors preserved their visually recognizable character and their performance is comparable to the one achieved in simulation.

Abstract:
Cooperative perception lets agents share information to expand coverage and improve scene understanding. However, in real‑world scenarios, diverse and unpredictable corruptions undermine its robustness and generalization. To address these challenges, we introduce CoopDiff, a diffusion‑based cooperative perception framework that mitigates corruptions via a denoising mechanism. CoopDiff adopts a teacher‑student paradigm: the Quality‑Aware Teacher performs voxel‑level early fusion with Quality of Interest weighting and semantic guidance, then produces clean supervision features via a diffusion denoiser. The Dual‑Branch Diffusion Student first separates ego and cooperative streams in encoding to reconstruct the teacher's clean targets. And then, an Ego‑Guided Cross‑Attention mechanism facilitates balanced decoding under degradation by adaptively integrating ego and cooperative features. We evaluate CoopDiff on two constructed multi‑degradation benchmarks, OPV2Vn and DAIR‑V2Xn, each incorporating six corruption types, including environmental and sensor‑level distortions. Benefiting from the inherent denoising properties of diffusion, CoopDiff consistently outperforms prior methods across all degradation types and lowers the relative corruption error. Furthermore, it offers a tunable balance between precision and inference efficiency.

Abstract:
Occlusions present a significant challenge for connected and automated vehicles, as they can obscure critical road users from perception systems. Traditional risk metrics often fail to capture the cumulative nature of these threats over time adequately. In this paper, we propose a novel and universal risk assessment metric, the Risk of Tracking Loss (RTL), which aggregates instantaneous risk intensity throughout occluded periods. This provides a holistic risk profile that encompasses both high‑intensity, short‑term threats and prolonged exposure. Utilizing diverse and high‑fidelity real‑world datasets, a large‑scale statistical analysis is conducted to characterize occlusion risk and validate the effectiveness of the proposed metric. The metric is applied to evaluate different vehicle‑to‑everything (V2X) deployment strategies. Our study shows that full V2X penetration theoretically eliminates this risk, the reduction is highly nonlinear; a substantial statistical benefit requires a high penetration threshold of 75‑90%. To overcome this limitation, we propose a novel asymmetric communication framework that allows even non‑connected vehicles to receive warnings. Experimental results demonstrate that this paradigm achieves better risk mitigation performance. We found that our approach at 25% penetration outperforms the traditional symmetric model at 75%, and benefits saturate at only 50% penetration. This work provides a crucial risk assessment metric and a cost‑effective, strategic roadmap for accelerating the safety benefits of V2X deployment.

Abstract:
Coordinated collective motion in bird flocks and fish schools inspires algorithms for cohesive swarm robotics. This paper presents a position‑based flocking model that achieves persistent velocity alignment without velocity sensing. By approximating relative velocity differences from changes between current and initial relative positions and incorporating a time‑ and density‑dependent alignment gain with a non‑zero minimum threshold to maintain persistent alignment, the model sustains coherent collective motion over extended periods. Simulations with a collective of 50 agents demonstrate that the position‑based flocking model attains faster and more sustained directional alignment and results in more compact formations than a velocity‑alignment‑based baseline. This position‑based flocking model is particularly well‑suited for real‑world robotic swarms, where velocity measurements are unreliable, noisy, or unavailable. Experimental results using a team of nine real wheeled mobile robots are also presented.

Abstract:
We present a coverage framework that integrates Hilbert space‑filling priors into decentralized multi‑robot learning and execution. We augment DQN and PPO with Hilbert‑based spatial indices to structure exploration and reduce redundancy in sparse‑reward environments, and we evaluate scalability in multi‑robot grid coverage. We further describe a waypoint interface that converts Hilbert orderings into curvature‑bounded, time‑parameterized SE(2) trajectories (planar (x, y, θ)), enabling onboard feasibility on resource‑constrained robots. Experiments show improvements in coverage efficiency, redundancy, and convergence speed over DQN/PPO baselines. In addition, we validate the approach on a Boston Dynamics Spot legged robot, executing the generated trajectories in indoor environments and observing reliable coverage with low redundancy. These results indicate that geometric priors improve autonomy and scalability for swarm and legged robotics.

Abstract:
Reliable localization is a fundamental requirement for multi‑robot systems operating in GPS‑denied environments. Visual‑inertial odometry (VIO) provides lightweight and accurate motion estimation but suffers from cumulative drift in the absence of global references. Ultra‑wideband (UWB) ranging offers complementary global observations, yet most existing UWB‑aided VIO methods are designed for single‑robot scenarios and rely on pre‑calibrated anchors, which limits their robustness in practice. This paper proposes a distributed collaborative visual‑inertial‑ranging odometry (DC‑VIRO) framework that tightly fuses VIO and UWB measurements across multiple robots. Anchor positions are explicitly included in the system state to address calibration uncertainty, while shared anchor observations are exploited through inter‑robot communication to provide additional geometric constraints. By leveraging a right‑invariant error formulation on Lie groups, the proposed approach preserves the observability properties of standard VIO, ensuring estimator consistency. Simulation results with multiple robots demonstrate that DC‑VIRO significantly improves localization accuracy and robustness, while simultaneously enabling anchor self‑calibration in distributed settings.

Abstract:
Most multi‑robot task allocation methods rely on communication to resolve conflicts and reach consistent assignments. In environments with limited bandwidth, degraded infrastructure, or adversarial interference, existing approaches degrade sharply. We introduce a learning‑based framework that achieves high‑quality task allocation without any robot‑to‑robot communication. The key idea is that robots coordinate implicitly by predicting teammates' bids: if each robot can anticipate competition for a task, it can adjust its choices accordingly. Our method predicts bid distributions to correct systematic errors in analytical mean‑field approximations. While analytical predictions assume idealized conditions (uniform distributions, known bid functions), our learned approach adapts to task clustering and spatial heterogeneity. Inspired by Smart Predict‑then‑Optimize (SPO), we train predictors end‑to‑end to minimize Task Allocation Regret rather than prediction error. To scale to large swarms, we develop a mean‑field approximation where each robot predicts the distribution of competing bids rather than individual bids, reducing complexity from O(NT) to O(T). We call our approach FORMICA: Field‑Oriented Regret‑Minimizing Implicit Coordination Algorithm. Experiments show FORMICA substantially outperforms a natural analytical baseline. In scenarios with 16 robots and 64 tasks, our approach improves system reward by 17% and approaches the optimal MILP solution. When deployed on larger scenarios (256 robots, 4096 tasks), the same model improves performance by 7%, demonstrating strong generalization. Training requires only 21 seconds on a laptop, enabling rapid adaptation to new environments.

Abstract:
The capability of multi‑robot SLAM approaches to merge localization history and maps from different observers is often challenged by the difficulty in establishing data association. Loop closure detection between perceptual inputs of different robotic agents is easily compromised in the context of perceptual aliasing, or when perspectives differ significantly. For this reason, direct mutual observation among robots is a powerful way to connect partial SLAM graphs, but often relies on the presence of calibrated arrays of fiducial markers (e.g., AprilTag arrays), which severely limits the range of observations and frequently fails under sharp lighting conditions, e.g., reflections or overexposure. In this work, we propose a novel solution to this problem leveraging recent advances in Deep‑Learning‑based 6D pose estimation. We feature markerless pose estimation as part of a decentralized multi‑robot SLAM system and demonstrate the benefit to the relative localization accuracy among the robotic team. The solution is validated experimentally on data recorded in a test field campaign on a planetary analogous environment.

Abstract:
Deploying heterogeneous robot teams to accomplish multiple tasks over extended time horizons presents significant computational challenges for task allocation and planning. In this paper, we present a comprehensive, time‑extended, offline heterogeneous multi‑robot task allocation framework, TRAITS, which we believe to be the first that can cope with the provisioning of exhaustible traits under battery and temporal constraints. Specifically, we introduce a nonlinear programming‑based trait distribution module that can optimize the trait‑provisioning rate of coalitions to yield feasible and time‑efficient solutions. TRAITS provides a more accurate feasibility assessment and estimation of task execution times and makespan by leveraging trait‑provisioning rates while optimizing battery consumption ‑‑ an advantage that state‑of‑the‑art frameworks lack. We evaluate TRAITS against two state‑of‑the‑art frameworks, with results demonstrating its advantage in satisfying complex trait and battery requirements while remaining computationally tractable.

Abstract:
Cooperative perception systems for autonomous driving aim to overcome the limited perception range of a single vehicle by communicating with adjacent agents to share sensing information. While this improves perception performance, these systems also face a significant privacy‑leakage issue, as sensitive visual content can potentially be reconstructed from the shared data. In this paper, we propose a novel Privacy‑Concealing Cooperation (PCC) framework for Bird's Eye View (BEV) semantic segmentation. Based on commonly shared BEV features, we design a hiding network to prevent an image reconstruction network from recovering the input images from the shared features. An adversarial learning mechanism is employed to train the network, where the hiding network works to conceal the visual clues in the BEV features while the reconstruction network attempts to uncover these clues. To maintain segmentation performance, the perception network is integrated with the hiding network and optimized end‑to‑end. The experimental results demonstrate that the proposed PCC framework effectively degrades the quality of the reconstructed images with minimal impact on segmentation performance, providing privacy protection for cooperating vehicles. The source code will be made publicly available upon publication.

Abstract:
Cooperative perception enables autonomous agents to share encoded representations over wireless communication to enhance each other's live situational awareness. However, the tension between the limited communication bandwidth and the rich sensor information hinders its practical deployment. Recent studies have explored selection strategies that share only a subset of features per frame while striving to keep the performance on par. Nevertheless, the bandwidth requirement still stresses current wireless technologies. To fundamentally ease the tension, we take a proactive approach, exploiting the temporal continuity to identify features that capture environment dynamics, while avoiding repetitive and redundant transmission of static information. By incorporating temporal awareness, agents are empowered to dynamically adapt the sharing quantity according to environment complexity. We instantiate this intuition into an adaptive selection framework, COOPERTRIM, which introduces a novel conformal temporal uncertainty metric to gauge feature relevance, and a data‑driven mechanism to dynamically determine the sharing quantity. To evaluate COOPERTRIM, we take semantic segmentation and 3D detection as example tasks. Across multiple open‑source cooperative segmentation and detection models, COOPERTRIM achieves up to 80.28% and 72.52% bandwidth reduction respectively while maintaining a comparable accuracy. Relative to other selection strategies, COOPERTRIM also improves IoU by as much as 45.54% with up to 72% less bandwidth. Combined with compression strategies, COOPERTRIM can further reduce bandwidth usage to as low as 1.46% without compromising IoU performance. Qualitative results show COOPERTRIM gracefully adapts to environmental dynamics, localization error, and communication latency, demonstrating flexibility and paving the way for real‑world deployment.

Abstract:
Collective behaviours often need to be expressed through numerical features, e.g., for classification or imitation learning. This problem is often addressed by proposing an ad‑hoc feature set for a particular swarm behaviour context, usually without further consideration of the solution's resilience outside of the conceived context. Yet, the development of automatic methods to design swarm behaviours is dependent on the ability to measure quantitatively the similarity of swarm behaviours. Hence, we investigate the impact of feature sets for collective behaviours. We select swarm feature sets and similarity measures from prior swarm robotics works, which mainly considered a narrow behavioural context and assess their robustness. We demonstrate that the interplay of feature set and similarity measure makes some combinations more suitable to distinguish groups of similar behaviours. We also propose a self‑organised map‑based approach to identify regions of the feature space where behaviours cannot be easily distinguished.

Abstract:
Multi‑robot systems require scalable and federated methods to model complex environments under computational and communication constraints. Gaussian Processes (GPs) offer robust probabilistic modeling, but suffer from cubic computational complexity, limiting their applicability in large‑scale deployments. To address this challenge, we introduce the pxpGP, a novel distributed GP framework tailored for both centralized and decentralized large‑scale multi‑robot networks. Our approach leverages sparse variational inference to generate a local compact pseudo‑representation. We introduce a sparse variational optimization scheme that bounds local pseudo‑datasets and formulate a global scaled proximal‑inexact consensus alternating direction method of multipliers (ADMM) with adaptive parameter updates and warm‑start initialization. Experiments on synthetic and real‑world datasets demonstrate that pxpGP and its decentralized variant, dec‑pxpGP, outperform existing distributed GP methods in hyperparameter estimation and prediction accuracy, particularly in large‑scale networks.

Abstract:
Dynamic maps (DM) serve as the fundamental information infrastructure for vehicle‑road‑cloud (VRC) cooperative autonomous driving in China and Japan. By providing comprehensive traffic scene representations, DM overcome the limitations of standalone autonomous driving systems (ADS), such as physical occlusions. Although DM‑enhanced ADS have been successfully deployed in real‑world applications in Japan, existing DM systems still lack a natural‑language‑supported (NLS) human interface, which could substantially enhance human‑DM interaction. To address this gap, this paper introduces VRCsim, a VRC cooperative perception (CP) simulation framework designed to generate streaming VRC‑CP data. Based on VRCsim, we construct a question‑answering data set, VRC‑QA, focused on spatial querying and reasoning in mixed‑traffic scenes. Building upon VRCsim and VRC‑QA, we further propose Talk2DM, a plug‑and‑play module that extends VRC‑DM systems with NLS querying and commonsense reasoning capabilities. Talk2DM is built upon a novel chain‑of‑prompt (CoP) mechanism that progressively integrates human‑defined rules with the commonsense knowledge of large language models (LLMs). Experiments on VRC‑QA show that Talk2DM can seamlessly switch across different LLMs while maintaining high NLS query accuracy, demonstrating strong generalization capability. Although larger models tend to achieve higher accuracy, they incur significant efficiency degradation. Our results reveal that Talk2DM, powered by Qwen3:8B, Gemma3:27B, and GPT‑oss models, achieves over 93% NLS query accuracy with an average response time of only 2‑5 seconds, indicating strong practical potential.

Abstract:
Modern applications, such as orchestrating the collective behavior of robotic swarms or traffic flows, require the coordination of large groups of agents evolving in unstructured environments, where disturbances and unmodeled dynamics are unavoidable. In this work, we develop a scalable macroscopic density control framework in which a feedback law is designed directly at the level of an advection‑‑diffusion partial differential equation. We formulate the control problem in the density space and prove global exponential convergence towards the desired behavior in \mathcalL^2 with guaranteed asymptotic rejection of bounded unknown drift terms, explicitly accounting for heterogeneous agent dynamics, unmodeled behaviors, and environmental perturbations. Our theoretical findings are corroborated by numerical experiments spanning heterogeneous oscillators, traffic systems, and swarm robotics in partially unknown environments.

Abstract:
Multi‑robot systems (MRS) rely on exchanging raw sensory data to cooperate in complex three‑dimensional (3D) environments. However, this strategy often leads to severe communication congestion and high transmission latency, significantly degrading collaboration efficiency. This paper proposes a decentralized task‑oriented semantic communication framework for multi‑robot collaboration in unknown 3D environments. Each robot locally extracts compact, task‑relevant semantics using a lightweight Pixel Difference Network (PiDiNet) with geometric processing. It shares only these semantic updates to build a task‑sufficient 3D scene representation that supports cooperative perception, navigation, and object transport. Our numerical results show that the proposed method exhibits a dramatic reduction in communication overhead from 858.6 Mb to 4.0 Mb (over 200× compression gain) while improving collaboration efficiency by shortening task completion from 1,054 to 281 steps.

Abstract:
This study presents an internalized morphogenesis model for autonomous systems, such as swarm robotics and micro‑nanomachines, that eliminates the need for external spatial computation. Traditional self‑organizing models often require calculations across the entire coordinate space, including empty areas, which is impractical for resource‑constrained physical modules. Our proposed model achieves complex morphogenesis through strictly local interactions between adjacent modules within the "body." By extending the "Ishida token model," modules exchange integer values using an RD‑inspired discrete analogue without solving differential equations. The internal potential, derived from token accumulation and aging, guides autonomous growth, shrinkage, and replication. Simulations on a hexagonal grid demonstrated the emergence of limb‑like extensions, self‑division, and robust regeneration capabilities following structural amputation. A key feature is the use of the body boundary as a natural sink for information entropy (tokens) to maintain a dynamic equilibrium. These results indicate that sophisticated morphological behaviors can emerge from minimal, internal‑only rules. This framework offers a computationally efficient and biologically plausible approach to developing self‑repairing, adaptive, and autonomous hardware.

Abstract:
This paper addresses robotic system engineering for safety‑ and mission‑critical applications by bridging the gap between high‑level objectives and formal, executable specifications. The proposed method, Robotic System Task to Model Transformation Methodology (RSTM2) is an ontology‑driven, hierarchical approach using stochastic timed Petri nets with resources, enabling Monte Carlo simulations at mission, system, and subsystem levels. A hypothetical case study demonstrates how the RSTM2 method supports architectural trades, resource allocation, and performance analysis under uncertainty. Ontological concepts further enable explainable AI‑based assistants, facilitating fully autonomous specification synthesis. The methodology offers particular benefits to complex multi‑robot systems, such as the NASA CADRE mission, representing decentralized, resource‑aware, and adaptive autonomous systems of the future.

Abstract:
Heterogeneous multi‑robot systems are increasingly used in long‑horizon missions requiring coordinated planning across diverse capabilities. However, existing planning approaches struggle to construct accurate symbolic representations and maintain plan consistency in dynamic environments. Classical PDDL planners require manually crafted symbolic models, while LLM‑based planners often ignore agent heterogeneity and environmental uncertainty. We introduce KGLAMP, a knowledge‑graph‑guided LLM planning framework for heterogeneous multi‑robot teams. The framework maintains a structured knowledge graph encoding object relations, spatial reachability, and robot capabilities, which guides the LLM in generating accurate PDDL problem specifications. The knowledge graph serves as a persistent, dynamically updated memory that incorporates new observations and triggers replanning upon detecting inconsistencies, enabling symbolic plans to adapt to evolving world states. Experiments on the MAT‑THOR benchmark show that KGLAMP improves performance by at least 25.3% over both LLM‑only and PDDL‑based variants.

Abstract:
Understanding self‑organization in natural collectives such as bird flocks inspires swarm robotics, yet most flocking models remain reactive, overlooking anticipatory cues that enhance coordination. Motivated by avian postural and wingbeat signals, as well as multirotor attitude tilts that precede directional changes, this work introduces a principled, bio‑inspired anticipatory augmentation of reactive flocking termed Future Direction‑Aware (FDA) flocking. In the proposed framework, agents blend reactive alignment with a predictive term based on short‑term estimates of neighbors' future velocities, regulated by a tunable blending parameter that interpolates between reactive and anticipatory behaviors. This predictive structure enhances velocity consensus and cohesion‑separation balance while mitigating the adverse effects of sensing and communication delays and measurement noise that destabilize reactive baselines. Simulation results demonstrate that FDA achieves faster and higher alignment, enhanced translational displacement of the flock, and improved robustness to delays and noise compared to a purely reactive model. Future work will investigate adaptive blending strategies, weighted prediction schemes, and experimental validation on multirotor drone swarms.

Abstract:
Traffic‑density matrices from different days exhibit both low rank and stable correlations in their singular‑vector subspaces. Leveraging this, we introduce SATORIS‑N, a framework for imputing partially observed traffic‑density by informed subspace priors from neighboring days. Our contribution is a subspace‑aware semidefinite programming (SDP) formulation of nuclear norm that explicitly informs the reconstruction with prior singular‑subspace information. This convex formulation jointly enforces low rank and subspace alignment, providing a single global optimum and substantially improving accuracy under medium and high occlusion. We also study a lightweight implicit subspace‑alignment strategy in which matrices from consecutive days are concatenated to encourage alignment of spatial or temporal singular directions. Although this heuristic offers modest gains when missing rates are low, the explicit SDP approach is markedly more robust when large fractions of entries are missing. Across two real‑world datasets (Beijing and Shanghai), SATORIS‑N consistently outperforms standard matrix‑completion methods such as SoftImpute, IterativeSVD, statistical, and even deep learning baselines at high occlusion levels. The framework generalizes to other spatiotemporal settings in which singular subspaces evolve slowly over time. In the context of intelligent vehicles and vehicle‑to‑everything (V2X) systems, accurate traffic‑density reconstruction enables critical applications including cooperative perception, predictive routing, and vehicle‑to‑infrastructure (V2I) communication optimization. When infrastructure sensors or vehicle‑reported observations are incomplete ‑ due to communication dropouts, sensor occlusions, or sparse connected vehicle penetration‑reliable imputation becomes essential for safe and efficient autonomous navigation.

Abstract:
We address the problem of how individuals can integrate efficiently their private behavior with information provided by others within a group. To this end, we consider the model of collective search introduced in [https://doi.org/10.1103/PhysRevE.102.012402], under a minimal setting with no olfactory information. Agents combine a private exploratory behavior and a social imitation consisting in aligning to their neighbors, and weigh the two contributions with a single ``trust" parameter that controls their relative influence. We find that an optimal trust parameter exists even in the absence of olfactory information, as was observed in the original model. Optimality is dictated by the need to explore the minimal region of space that contains the target. An optimal trust parameter emerges from this constraint because it it tunes imitation, which induces a collective mechanism of inertia affecting the size and path of the swarm. We predict the optimal trust parameter for cohesive groups where all agents interact with one another. We show how optimality depends on the initialization of the agents and the unknown location of the target, in close agreement with numerical simulations. Our results may be leveraged to optimize the design of swarm robotics or to understand information integration in organisms with decentralized nervous systems such as cephalopods.

Abstract:
Multi‑robot systems have been widely deployed in real‑world applications, providing significant improvements in efficiency and reductions in labor costs. However, most existing multi‑robot collaboration methods rely on extensive task‑specific training, which limits their adaptability to new or diverse scenarios. Recent research leverages the language understanding and reasoning capabilities of large language models (LLMs) to enable more flexible collaboration without specialized training. Yet, current LLM‑empowered approaches remain inefficient: when confronted with identical or similar tasks, they must replan from scratch because they omit task‑level similarities. To address this limitation, we propose MeCo, a similarity‑aware multi‑robot collaboration framework that applies the principle of ``cache and reuse'' (a.k.a., memoization) to reduce redundant computation. Unlike simple task repetition, identifying and reusing solutions for similar but not identical tasks is far more challenging, particularly in multi‑robot settings. To this end, MeCo introduces a new similarity testing method that retrieves previously solved tasks with high relevance, enabling effective plan reuse without re‑invoking LLMs. Furthermore, we present MeCoBench, the first benchmark designed to evaluate performance on similar‑task collaboration scenarios. Experimental results show that MeCo substantially reduces planning costs and improves success rates compared with state‑of‑the‑art approaches.

Abstract:
As a core technology of intelligent transportation systems, vehicular ad‑hoc networks support latency‑sensitive services such as safety warning and cooperative perception via vehicle‑to‑everything communications. However, their highly dynamic topology increases average path length, raises latency, and reduces throughput, severely limiting communication performance. Existing topology optimization methods lack capabilities in multi‑objective coordination, dynamic adaptation, and global‑local synergy. To address this, this paper proposes a two‑layer dynamic topology regulation scheme combining local feature aggregation and global adjustment. The scheme constructs a dynamic multi‑objective optimization model integrating average path length, end‑to‑end latency, and network throughput, and achieves multi‑index coordination via link adaptability metrics and a dynamic normalization mechanism. it quickly responds to local link changes via feature fusion of local node feature extraction and dynamic neighborhood sensing, and balances optimization accuracy and real‑time performance using a dual‑mode adaptive solving strategy for global topology adjustment. It reduces network oscillation risks by introducing a performance improvement threshold and a topology validity verification mechanism. Simulation results on real urban road networks via the SUMO platform show that the proposed scheme outperforms traditional methods in average path length (stabilizing at ~4 hops), end‑to‑end latency (remaining ~0.01 s), and network throughput.

Abstract:
The increasing complexity of marine operations has intensified the need for intelligent robotic systems to support ocean observation, exploration, and resource management. Underwater swarm robotics offers a promising framework that extends the capabilities of individual autonomous platforms through collective coordination. Inspired by natural systems, such as fish schools and insect colonies, bio‑inspired swarm approaches enable distributed decision‑making, adaptability, and resilience under challenging marine conditions. Yet research in this field remains fragmented, with limited integration across algorithmic, communication, and hardware design perspectives. This review synthesises bio‑inspired coordination mechanisms, communication strategies, and system design considerations for underwater swarm robotics. It examines key marine‑specific algorithms, including the Artificial Fish Swarm Algorithm, Whale Optimisation Algorithm, Coral Reef Optimisation, and Marine Predators Algorithm, highlighting their applications in formation control, task allocation, and environmental interaction. The review also analyses communication constraints unique to the underwater domain and emerging acoustic, optical, and hybrid solutions that support cooperative operation. Additionally, it examines hardware and system design advances that enhance system efficiency and scalability. A multi‑dimensional classification framework evaluates existing approaches across communication dependency, environmental adaptability, energy efficiency, and swarm scalability. Through this integrated analysis, the review unifies bio‑inspired coordination algorithms, communication modalities, and system design approaches. It also identifies converging trends, key challenges, and future research directions for real‑world deployment of underwater swarm systems.

Abstract:
Multi‑robot formation control has various applications in domains such as vehicle troops, platoons, payload transportation, and surveillance. Maintaining formation in a vehicle platoon requires designing a suitable control scheme that can tackle external disturbances and uncertain system parameters while maintaining a predefined safe distance between the robots. A crucial challenge in this context is dealing with the unknown/uncertain friction forces between wheels and the ground, which vary with changes in road surface, wear in tires, and speed of the vehicle. Although state‑of‑the‑art adaptive controllers can handle a priori bounded uncertainties, they struggle with accurately modeling and identifying frictional forces, which are often state‑dependent and cannot be a priori bounded. This thesis proposes a new adaptive sliding mode controller for wheeled mobile robot‑based vehicle platoons that can handle the unknown and complex behavior of frictional forces without prior knowledge of their parameters and structures. The controller uses the adaptive sliding mode control techniques to regulate the platoon's speed and maintain a predefined inter‑robot distance, even in the presence of external disturbances and uncertain system parameters. This approach involves a two‑stage process: first, the kinematic controller calculates the desired velocities based on the desired trajectory; and second, the dynamics model generates the commands to achieve the desired motion. By separating the kinematics and dynamics of the robot, this approach can simplify the control problem and allow for more efficient and robust control of the wheeled mobile robot.

Abstract:
Multi‑robot systems can greatly enhance efficiency through coordination and collaboration, yet in practice, full‑time communication is rarely available and interactions are constrained to close‑range exchanges. Existing methods either maintain all‑time connectivity, rely on fixed schedules, or adopt pairwise protocols, but none adapt effectively to dynamic spatio‑temporal task distributions under limited communication, resulting in suboptimal coordination. To address this gap, we propose CoCoPlan, a unified framework that co‑optimizes collaborative task planning and team‑wise intermittent communication. Our approach integrates a branch‑and‑bound architecture that jointly encodes task assignments and communication events, an adaptive objective function that balances task efficiency against communication latency, and a communication event optimization module that strategically determines when, where and how the global connectivity should be re‑established. Extensive experiments demonstrate that it outperforms state‑of‑the‑art methods by achieving a 22.4% higher task completion rate, reducing communication overhead by 58.6%, and improving the scalability by supporting up to 100 robots in dynamic environments. Hardware experiments include the complex 2D office environment and large‑scale 3D disaster‑response scenario.

Abstract:
Rapid motorization in emerging economies such as India has created severe enforcement asymmetries, with over 11 million recorded violations in 2023 against a human policing density of roughly one officer per 4000 vehicles. Traditional surveillance and manual ticketing cannot scale to this magnitude, motivating the need for an autonomous, cooperative, and energy efficient edge AI perception infrastructure. This paper presents a real time roadside perception node for multi class traffic violation analytics and safety event dissemination within a connected and intelligent vehicle ecosystem. The node integrates YOLOv8 Nano for high accuracy multi object detection, DeepSORT for temporally consistent vehicle tracking, and a rule guided OCR post processing engine capable of recognizing degraded or multilingual license plates compliant with MoRTH AIS 159 and ISO 7591 visual contrast standards. Deployed on an NVIDIA Jetson Nano with a 128 core Maxwell GPU and optimized via TensorRT FP16 quantization, the system sustains 28 to 30 frames per second inference at 9.6 W, achieving 97.7 percent violation detection accuracy and 84.9 percent OCR precision across five violation classes, namely signal jumping, zebra crossing breach, wrong way driving, illegal U turn, and speeding, without manual region of interest calibration. Comparative benchmarking against YOLOv4 Tiny, PP YOLOE S, and Nano DetPlus demonstrates a 10.7 percent mean average precision gain and a 1.4 times accuracy per watt improvement. Beyond enforcement, the node publishes standardized safety events of CAM and DENM type to connected vehicles and intelligent transportation system backends via V2X protocols, demonstrating that roadside edge AI analytics can augment cooperative perception and proactive road safety management within the IEEE Intelligent Vehicles ecosystem.

Abstract:
Morphological computing, the use of the physical design of a robot to ease the realization of a given task has been proven to be a relevant concept in the context of swarm robotics. Here we demonstrate both experimentally and numerically, that the success of such a strategy may heavily rely on the type of policy adopted by the robots, as well as on the details of the physical design. To do so, we consider a swarm of robots, composed of Kilobots embedded in an exoskeleton, the design of which controls the propensity of the robots to align or anti‑align with the direction of the external force they experience. We find experimentally that the contrast that was observed between the two morphologies in the success rate of a simple phototactic task, where the robots were programmed to stop when entering a light region, becomes dramatic, if the robots are not allowed to stop, and can only slow down. Building on a faithful physical model of the self‑aligning dynamics of the robots, we perform numerical simulations and demonstrate on one hand that a precise tuning of the self‑aligning strength around a sweet spot is required to achieve an efficient phototactic behavior, on the other hand that exploring a range of self‑alignment strength allows for a rich expressivity of collective behaviors.

Abstract:
Collaborative perception (CP) is a critical technology in applications like autonomous driving and smart cities. It involves the sharing and fusion of information among sensors to overcome the limitations of individual perception, such as blind spots and range limitations. However, CP faces two primary challenges. First, due to the dynamic nature of the environment, the timeliness of the transmitted information is critical to perception performance. Second, with limited computational power at the sensors and constrained wireless bandwidth, the communication volume must be carefully designed to ensure feature representations are both effective and sufficient. This work studies the dynamic scheduling problem in a multi‑region CP scenario, and presents a Timeliness‑Aware Multi‑region Prioritized (TAMP) scheduling algorithm to trade‑off perception accuracy and communication resource usage. Timeliness reflects the utility of information that decays as time elapses, which is manifested by the perception performance in CP tasks. We propose an empirical penalty function that maps the joint impact of Age of Information (AoI) and communication volume to perception performance. Aiming to minimize this timeliness‑oriented penalty in the long‑term, and recognizing that scheduling decisions have a cumulative effect on subsequent system states, we propose the TAMP scheduling algorithm. TAMP is a Lyapunov‑based optimization policy that decomposes the long‑term average objective into a per‑slot prioritization problem, balancing the scheduling worth against resource cost. We validate our algorithm in both intersection and corridor scenarios with the real‑world Roadside Cooperative perception (RCooper) dataset. Extensive simulations demonstrate that TAMP outperforms the best‑performing baseline, achieving an Average Precision (AP) improvement of up to 27% across various configurations.

Abstract:
Complex multi‑robot missions often require heterogeneous teams to jointly optimize task allocation, scheduling, and path planning to improve team performance under strict constraints. We formalize these complexities into a new class of problems, dubbed Spatio‑Temporal Efficacy‑optimized Allocation for Multi‑robot systems (STEAM). STEAM builds upon trait‑based frameworks that model robots using their capabilities (e.g., payload and speed), but goes beyond the typical binary success‑failure model by explicitly modeling the efficacy of allocations as trait‑efficacy maps. These maps encode how the aggregated capabilities assigned to a task determine performance. Further, STEAM accommodates spatio‑temporal constraints, including a user‑specified time budget (i.e., maximum makespan). To solve STEAM problems, we contribute a novel algorithm named Efficacy‑optimized Incremental Task Allocation Graph Search (E‑ITAGS) that simultaneously optimizes task performance and respects time budgets by interleaving task allocation, scheduling, and path planning. Motivated by the fact that trait‑efficacy maps are difficult, if not impossible, to specify, E‑ITAGS efficiently learns them using a realizability‑aware active learning module. Our approach is realizability‑aware since it explicitly accounts for the fact that not all combinations of traits are realizable by the robots available during learning. Further, we derive experimentally‑validated bounds on E‑ITAGS' suboptimality with respect to efficacy. Detailed numerical simulations and experiments using an emergency response domain demonstrate that E‑ITAGS generates allocations of higher efficacy compared to baselines, while respecting resource and spatio‑temporal constraints. We also show that our active learning approach is sample efficient and establishes a principled tradeoff between data and computational efficiency.

Abstract:
Multi‑robot systems are widely used for coverage tasks that require efficient coordination across large environments. In Multi‑Robot Coverage Path Planning (MCPP), the objective is typically to minimize the makespan by generating non‑overlapping paths for full‑area coverage. However, most existing methods assume uniform importance across regions, limiting their effectiveness in scenarios where some zones require faster attention. We introduce the Priority‑Aware MCPP (PA‑MCPP) problem, where a subset of the environment is designated as prioritized zones with associated weights. The goal is to minimize, in lexicographic order, the total priority‑weighted latency of zone coverage and the overall makespan. To address this, we propose a scalable two‑phase framework combining (1) greedy zone assignment with local search, spanning‑tree‑based path planning, and (2) Steiner‑tree‑guided residual coverage. Experiments across diverse scenarios demonstrate that our method significantly reduces priority‑weighted latency compared to standard MCPP baselines, while maintaining competitive makespan. Sensitivity analyses further show that the method scales well with the number of robots and that zone coverage behavior can be effectively controlled by adjusting priority weights.

Abstract:
Relative localization is critical for cooperation in autonomous multi‑robot systems. Existing approaches either rely on shared environmental features or inertial assumptions or suffer from non‑line‑of‑sight degradation and outliers in complex environments. Robust and efficient fusion of inter‑robot measurements such as bearings, distances, and inertials for tens of robots remains challenging. We present CREPES‑X (Cooperative RElative Pose Estimation System with multiple eXtended features), a hierarchical relative localization framework that enhances speed, accuracy, and robustness under challenging conditions, without requiring any global information. CREPES‑X starts with a compact hardware design: InfraRed (IR) LEDs, an IR camera, an ultra‑wideband module, and an IMU housed in a cube no larger than 6cm on each side. Then CREPES‑X implements a two‑stage hierarchical estimator to meet different requirements, considering speed, accuracy, and robustness. First, we propose a single‑frame relative estimator that provides instant relative poses for multi‑robot setups through a closed‑form solution and robust bearing outlier rejection. Then a multi‑frame relative estimator is designed to offer accurate and robust relative states by exploring IMU pre‑integration via robocentric relative kinematics with loosely‑ and tightly‑coupled optimization. Extensive simulations and real‑world experiments validate the effectiveness of CREPES‑X, showing robustness to up to 90% bearing outliers, proving resilience in challenging conditions, and achieving RMSE of 0.073m and 1.817° in real‑world datasets.

Abstract:
In collective systems, the available agents are a limited resource that must be allocated among tasks to maximize collective performance. Computing the optimal allocation of several agents to numerous tasks through a brute‑force approach can be infeasible, especially when each task's performance scales differently with the increase of agents. For example, difficult tasks may require more agents to achieve similar performances compared to simpler tasks, but performance may saturate nonlinearly as the number of allocated agents increases. We propose a computationally efficient algorithm, based on marginal performance gains, for optimally allocating agents to tasks with concave scalability functions, including linear, saturating, and retrograde scaling, to achieve maximum collective performance. We test the algorithm by allocating a simulated robot swarm among collective decision‑making tasks, where embodied agents sample their environment and exchange information to reach a consensus on spatially distributed environmental features. We vary task difficulties by different geometrical arrangements of environmental features in space (patchiness). In this scenario, decision performance in each task scales either as a saturating curve (following the Condorcet's Jury Theorem in an interference‑free setup) or as a retrograde curve (when physical interference among robots restricts their movement). Using simple robot simulations, we show that our algorithm can be useful in allocating robots among tasks. Our approach aims to advance the deployment of future real‑world multi‑robot systems.

Abstract:
Multi‑view cooperative perception and multimodal fusion are essential for reliable 3D spatiotemporal understanding in autonomous driving, especially under occlusions, limited viewpoints, and communication delays in V2X scenarios. This paper proposes XET‑V2X, a multi‑modal fused end‑to‑end tracking framework for v2x collaboration that unifies multi‑view multimodal sensing within a shared spatiotemporal representation. To efficiently align heterogeneous viewpoints and modalities, XET‑V2X introduces a dual‑layer spatial cross‑attention module based on multi‑scale deformable attention. Multi‑view image features are first aggregated to enhance semantic consistency, followed by point cloud fusion guided by the updated spatial queries, enabling effective cross‑modal interaction while reducing computational overhead. Experiments on the real‑world V2X‑Seq‑SPD dataset and the simulated V2X‑Sim‑V2V and V2X‑Sim‑V2I benchmarks demonstrate consistent improvements in detection and tracking performance under varying communication delays. Both quantitative results and qualitative visualizations indicate that XET‑V2X achieves robust and temporally stable perception in complex traffic scenarios.

Abstract:
Multi‑robot path planning is a fundamental yet challenging problem due to its combinatorial complexity and the need to balance global efficiency with fair task allocation among robots. Traditional swarm intelligence methods, although effective on small instances, often converge prematurely and struggle to scale to complex environments. In this work, we present a structure‑induced exploration framework that integrates structural priors into the search process of the ant colony optimization (ACO). The approach leverages the spatial distribution of the task to induce a structural prior at initialization, thereby constraining the search space. The pheromone update rule is then designed to emphasize structurally meaningful connections and incorporates a load‑aware objective to reconcile the total travel distance with individual robot workload. An explicit overlap suppression strategy further ensures that tasks remain distinct and balanced across the team. The proposed framework was validated on diverse benchmark scenarios covering a wide range of instance sizes and robot team configurations. The results demonstrate consistent improvements in route compactness, stability, and workload distribution compared to representative metaheuristic baselines. Beyond performance gains, the method also provides a scalable and interpretable framework that can be readily applied to logistics, surveillance, and search‑and‑rescue applications where reliable large‑scale coordination is essential.

Abstract:
Biological systems can form complex three‑dimensional structures through the collective behavior of agents that share a common update rule and operate without central control. How such distributed control gives rise to precise global patterns remains a central question not only in developmental biology but also in distributed robotics, programmable matter, and multi‑agent learning. Here, we introduce DiffeoMorph, an end‑to‑end differentiable framework for learning a morphogenesis protocol that guides a population of agents to morph into a target 3D shape. Each agent updates its position and internal state using an SE(3)‑equivariant graph neural network, based on its own internal state and signals received from other agents. To train this system, we introduce a new shape‑matching loss based on 3D Zernike polynomials, which compares the predicted and target shapes as continuous spatial distributions, not as discrete point clouds, and is invariant to agent ordering, number of agents, and global orientation. To achieve rotation invariance while preserving reflection sensitivity, we include an alignment step that optimally rotates the predicted Zernike spectrum to match the target before computing the loss. We perform benchmarking to establish the advantages of our shape‑matching loss over other standard distance metrics for shape comparison tasks. We then demonstrate that DiffeoMorph can form a range of complex shapes from minimally patterned initial conditions. DiffeoMorph provides a general framework for learning distributed control strategies for morphogenesis, swarm robotics, and programmable self‑assembly.

Abstract:
Cooperative perception between vehicles is poised to offer robust and reliable scene understanding. Recently, we are witnessing experimental systems research building testbeds that share raw spatial sensor data for cooperative perception. While there has been a marked improvement in accuracies and is the natural way forward, we take a moment to consider the problems with such an approach for eventual adoption by automakers. In this paper, we first argue that new forms of privacy concerns arise and discourage stakeholders to share raw sensor data. Next, we present SHARP, a research framework to minimize privacy leakage and drive stakeholders towards the ambitious goal of raw data based cooperative perception. Finally, we discuss open questions for networked systems, mobile computing, perception researchers, industry and government in realizing our proposed framework.

Abstract:
The efficacy of UAV swarm cooperative perception fundamentally depends on three‑dimensional (3D) formation geometry, which governs target observability and sensor complementarity. In the literature, the exploitation of formation geometry and its impact on UAV sensing have rarely been studied, which can significantly degrade multimodal cooperative perception at scenarios where heterogeneous payloads (vision cameras and LiDAR) should be geometrically arranged to exploit their complementary strengths while managing communication interference and hardware budgets. To bridge this critical gap, we propose an information‑theoretic optimization framework that allocation of UAVs and multimodal sensors, configures formation geometries, and flight control. The UAV‑sensor allocation is optimized by the Fisher Information Matrix (FIM) determinant maximization. Under this framework we introduce an equivalent formation transition strategy that enhances field‑of‑view (FOV) coverage without compromising perception accuracy and communication interference. Furthermore, we design a novel Lyapunov‑stable flight control scheme with logarithmic potential fields to generate energy‑efficient trajectories for formation transitions. Extensive simulations demonstrate our formation‑aware design achieves 25.0% improvement in FOV coverage, 104.2% enhancement in communication signal strength, and 47.2% reduction in energy consumption compared to conventional benchmarks. This work establishes that task‑driven geometric configuration represents a foundational rather than incidental component in next‑generation UAV swarm systems.

Abstract:
This paper investigates the control problem of steering a group of spherical mobile robots to cooperatively transport a spherical object. By controlling the movements of the robots to exert appropriate contact (pushing) forces, it is desired that the object follows a velocity command. To solve the problem, we first treat the robots' positions as virtual control inputs of the object, and propose a velocity‑tracking controller based on quadratic programming (QP), enabling the robots to cooperatively generate desired contact forces while minimizing the sum of the contact‑force magnitudes. Then, we design position‑tracking controllers for the robots. By appropriately designing the objective function and the constraints for the QP, it is guaranteed that the QP admits a unique solution and the QP‑based velocity‑tracking controller is Lipschitz continuous. Finally, we consider the closed‑loop system as an interconnection of two subsystems, corresponding to the velocity‑tracking error of the object and the position‑tracking error of the robots, and employ nonlinear small‑gain techniques for stability analysis. The effectiveness of the proposed design is demonstrated through numerical simulations.

Abstract:
Multi‑robot systems performing continuous tasks face a performance trade‑off when interrupted by urgent, time‑critical sub‑tasks. We investigate this trade‑off in a scenario where a team must balance area patrolling with locating an anomalous radio signal. To address this trade‑off, we evaluate both behavioral heterogeneity through agent role specialization ("patrollers" and "searchers") and sensing heterogeneity (i.e., only the searchers can sense the radio signal). Through simulation, we identify the Pareto‑optimal trade‑offs under varying team compositions, with behaviorally heterogeneous teams demonstrating the most balanced trade‑offs in the majority of cases. When sensing capability is restricted, heterogeneous teams with half of the sensing‑capable agents perform comparably to homogeneous teams, providing cost‑saving rationale for restricting sensor payload deployment. Our findings demonstrate that pre‑deployment role and sensing specialization are powerful design considerations for multi‑robot systems facing time‑conflicting tasks, where varying the degree of behavioral heterogeneity can tune system performance toward either task.

Authors: Jason Hughes, Marcel Hussing, Edward Zhang, Shenbagaraj Kannapiran, Joshua Caswell, Kenneth Chaney, Ruichen Deng, Michaela Feehery, Agelos Kratimenos, Yi Fan Li, Britny Major, Ethan Sanchez, Sumukh Shrote, Youkang Wang, Jeremy Wang, Daudi Zein, Luying Zhang, Ruijun Zhang, Alex Zhou, Tenzi Zhouga, Jeremy Cannon, Zaffir Qasim, Jay Yelon, Fernando Cladera, Kostas Daniilidis, Camillo J. Taylor, Eric Eaton

Abstract:
This report presents a heterogeneous robotic system designed for remote primary triage in mass‑casualty incidents (MCIs). The system employs a coordinated air‑ground team of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) to locate victims, assess their injuries, and prioritize medical assistance without risking the lives of first responders. The UAV identify and provide overhead views of casualties, while UGVs equipped with specialized sensors measure vital signs and detect and localize physical injuries. Unlike previous work that focused on exploration or limited medical evaluation, this system addresses the complete triage process: victim localization, vital sign measurement, injury severity classification, mental status assessment, and data consolidation for first responders. Developed as part of the DARPA Triage Challenge, this approach demonstrates how multi‑robot systems can augment human capabilities in disaster response scenarios to maximize lives saved.

Abstract:
Privacy has become a critical concern in modern multi‑robot systems, driven by both ethical considerations and operational constraints. As a result, growing attention has been directed toward privacy‑preserving coordination in dynamical multi‑robot systems. This work introduces a randomized scheduling mechanism for privacy‑preserving robot rendezvous. The proposed approach achieves improved privacy even at lower communication rates, where privacy is quantified via pointwise maximal leakage. We show that lower transmission rates provide stronger privacy guarantees and prove that rendezvous is still achieved under the randomized scheduling mechanism. Numerical simulations are provided to demonstrate the effectiveness of the method.

Abstract:
Visual Spatial Reasoning is crucial for enabling Multimodal Large Language Models (MLLMs) to understand object properties and spatial relationships, yet current models still struggle with 3D‑aware reasoning. Existing approaches typically enhance either perception, by augmenting RGB inputs with auxiliary modalities such as depth and segmentation, or reasoning, by training on spatial VQA datasets and applying reinforcement learning, and thus treat these two aspects in isolation. In this work, we investigate whether a unified MLLM can develop an intrinsic ability to enhance spatial perception and, through adaptive interleaved reasoning, achieve stronger spatial intelligence. We propose COOPER, a unified MLLM that leverages depth and segmentation as auxiliary modalities and is trained in two stages to acquire auxiliary modality generation and adaptive, interleaved reasoning capabilities. COOPER achieves an average 6.91% improvement in spatial reasoning while maintaining general performance. Moreover, even a variant trained only for auxiliary modality generation attains a 7.92% gain on distance and size estimation, suggesting that learning to generate auxiliary modalities helps internalize spatial knowledge and strengthen spatial understanding.

Abstract:
Multi‑robot task allocation in construction automation has traditionally relied on optimization methods such as Dynamic Programming and Reinforcement Learning. This research introduces the LangGraph‑based Task Allocation Agent (LTAA), an LLM‑driven framework that integrates phase‑adaptive allocation strategies, multi‑stage validation with hierarchical retries, and dynamic prompting for efficient robot coordination. Although recent LLM approaches show potential for construction robotics, they largely lack rigorous validation and benchmarking against established algorithms. This paper presents the first systematic comparison of LLM‑based task allocation with traditional methods in construction scenarios.The study validates LLM feasibility through SMART‑LLM replication and addresses implementation challenges using a Self‑Corrective Agent Architecture. LTAA leverages natural‑language reasoning combined with structured validation mechanisms, achieving major computational gains reducing token usage by 94.6% and allocation time by 86% through dynamic prompting. The framework adjusts its strategy across phases: emphasizing execution feasibility early and workload balance in later allocations.The authors evaluate LTAA against Dynamic Programming, Q‑learning, and Deep Q‑Network (DQN) baselines using construction operations from the TEACh human‑robot collaboration dataset. In the Heavy Excels setting, where robots have strong task specializations, LTAA achieves 77% task completion with superior workload balance, outperforming all traditional methods. These findings show that LLM‑based reasoning with structured validation can match established optimization algorithms while offering additional advantages such as interpretability, adaptability, and the ability to update task logic without retraining.

Abstract:
Coordinating emergency responses in extreme environments, such as wildfires, requires resilient and high‑bandwidth communication backbones. While autonomous aerial swarms can establish ad‑hoc networks to provide this connectivity, the high risk of individual node attrition in these settings often leads to network fragmentation and mission‑critical downtime. To overcome this challenge, we introduce and formalize the problem of Robust Task Networking Under Attrition (RTNUA), which extends connectivity maintenance in multi‑robot systems to explicitly address proactive redundancy and attrition recovery. We then introduce Physics‑Informed Robust Employment of Multi‑Agent Networks (ΦIREMAN), a topological algorithm leveraging physics‑inspired potential fields to solve this problem. In our evaluations, ΦIREMAN consistently outperforms baselines, and is able to maintain greater than 99.9% task uptime despite substantial attrition in simulations with up to 100 tasks and 500 drones, demonstrating both effectiveness and scalability.

Abstract:
Vehicle‑to‑Everything (V2X) cooperation is reshaping traffic safety from an ego‑centric sensing problem into one of collective intelligence. This survey structures recent progress within a unified Sensor‑Perception‑Decision (SPD) framework that formalizes how safety emerges from the interaction of distributed sensing, cooperative perception, and coordinated decision‑making across vehicles and infrastructure. Rather than centering on link protocols or message formats, we focus on how shared evidence, predictive reasoning, and human‑aligned interventions jointly enable proactive risk mitigation. Within this SPD lens, we synthesize advances in cooperative perception, multi‑modal forecasting, and risk‑aware planning, emphasizing how cross‑layer coupling turns isolated detections into calibrated, actionable understanding. Timing, trust, and human factors are identified as cross‑cutting constraints that determine whether predictive insights are delivered early enough, with reliable confidence, and in forms that humans and automated controllers can use. Compared with prior V2X safety surveys, this work (i) organizes the literature around a formal SPD safety loop and (ii) systematically analyzes research evolution and evaluation gaps through a PRISMA‑guided bibliometric study of hundreds of publications from 2016‑2025. The survey concludes with a roadmap toward cooperative safety intelligence, outlining SPD‑based design principles and evaluation practices for next‑generation V2X safety systems.

Abstract:
The continuous innovation of smart robotic technologies is driving the development of smart orchards, significantly enhancing the potential for automated harvesting systems. While multi‑robot systems offer promising solutions to address labor shortages and rising costs, the efficient scheduling of these systems presents complex optimization challenges. This research investigates the multi‑trip picking robot task scheduling (MTPRTS) problem. The problem is characterized by its provision for robot redeployment while maintaining strict adherence to makespan constraints, and encompasses the interdependencies among robot weight, robot load, and energy consumption, thus introducing substantial computational challenges that demand sophisticated optimization algorithms.To effectively tackle this complexity, metaheuristic approaches, which often utilize local search mechanisms, are widely employed. Despite the critical role of local search in vehicle routing problems, most existing algorithms are hampered by redundant local operations, leading to slower search processes and higher risks of local optima, particularly in large‑scale scenarios. To overcome these limitations, we propose an adaptive experience‑based discrete genetic algorithm (AEDGA) that introduces three key innovations: (1) integrated load‑distance balancing initialization method, (2) a clustering‑based local search mechanism, and (3) an experience‑based adaptive selection strategy. To ensure solution feasibility under makespan constraints, we develop a solution repair strategy implemented through three distinct frameworks. Comprehensive experiments on 18 proposed test instances and 24 existing test problems demonstrate that AEDGA significantly outperforms eight state‑of‑the‑art algorithms.

Abstract:
This paper presents a unified and scalable framework for predictive and safe autonomous navigation in dynamic transportation environments by integrating model predictive control (MPC) with distributed Koopman operator learning. High‑dimensional sensory data are employed to model and forecast the motion of surrounding dynamic obstacles. A consensus‑based distributed Koopman learning algorithm enables multiple computational agents or sensing units to collaboratively estimate the Koopman operator without centralized data aggregation, thereby supporting large‑scale and communication‑efficient learning across a networked system. The learned operator predicts future spatial densities of obstacles, which are subsequently represented through Gaussian mixture models. Their confidence ellipses are approximated by convex polytopes and embedded as linear constraints in the MPC formulation to guarantee safe and collision‑free navigation. The proposed approach not only ensures obstacle avoidance but also scales efficiently with the number of sensing or computational nodes, aligning with cooperative perception principles in autonomous navigation applications. Theoretical convergence guarantees and predictive constraint formulations are established, and extensive simulations demonstrate reliable, safe, and computationally efficient navigation performance in complex environments.

Abstract:
Effective collective decision‑making in swarm robotics often requires balancing exploration, communication and individual uncertainty estimation, especially in hazardous environments where direct measurements are limited or costly. We propose a decentralized Bayesian framework that enables a swarm of simple robots to identify the safer of two areas, each characterized by an unknown rate of hazardous events governed by a Poisson process. Robots employ a conjugate prior to gradually predict the times between events and derive confidence estimates to adapt their behavior. Our simulation results show that the robot swarm consistently chooses the correct area while reducing exposure to hazardous events by being sample‑efficient. Compared to baseline heuristics, our proposed approach shows better performance in terms of safety and speed of convergence. The proposed scenario has potential to extend the current set of benchmarks in collective decision‑making and our method has applications in adaptive risk‑aware sampling and exploration in hazardous, dynamic environments.

Abstract:
This paper addresses the challenge of coordinating multi‑robot systems under realistic communication delays using distributed optimization. We focus on consensus ADMM as a scalable framework for generating collision‑free, dynamically feasible motion plans in both trajectory optimization and receding‑horizon control settings. In practice, however, these algorithms are sensitive to penalty tuning or adaptation schemes (e.g. residual balancing and adaptive parameter heuristics) that do not explicitly consider delays. To address this, we introduce a Delay‑Aware ADMM (DA‑ADMM) variant that adapts penalty parameters based on real‑time delay statistics, allowing agents to down‑weight stale information and prioritize recent updates during consensus and dual updates. Through extensive simulations in 2D and 3D environments with double‑integrator, Dubins‑car, and drone dynamics, we show that DA‑ADMM significantly improves robustness, success rate, and solution quality compared to fixed‑parameter, residual‑balancing, and fixed‑constraint baselines. Our results highlight that performance degradation is not solely determined by delay length or frequency, but by the optimizer's ability to contextually reason over delayed information. The proposed DA‑ADMM achieves consistently better coordination performance across a wide range of delay conditions, offering a principled and efficient mechanism for resilient multi‑robot motion planning under imperfect communication.

Abstract:
Multi‑agent cooperative perception (CP) promises to overcome the inherent occlusion and range limitations of single‑agent systems in autonomous driving, yet its practicality is severely constrained by limited Vehicle‑to‑Everything (V2X) communication bandwidth. Existing approaches attempt to improve bandwidth efficiency via compression or heuristic message selection, but neglect the semantic relevance and cross‑agent redundancy of the transmitted data. In this paper, we formulate a joint semantic feature encoding and transmission problem that maximizes CP accuracy under a communication budget, and introduce JigsawComm, an end‑to‑end semantic‑aware framework that learns to ``assemble the puzzle'' of multi‑agent feature transmission. JigsawComm uses a regularized encoder to extract \emphsparse, semantically relevant features, and a lightweight Feature Utility Estimator (FUE) to predict each agent's per‑cell contribution to the downstream perception task. The FUE‑generated compact meta utility maps are exchanged among agents and used to compute an optimal transmission policy under the learned utility proxy. This policy inherently \empheliminates cross‑agent redundancy, bounding the feature transmission payload to \mathcalO(1) as the number of agents grows, while the meta information overhead remains negligible. The whole pipeline is trained end‑to‑end through a differentiable scheduling module, informing the FUE to be aligned with the task objective. On the OPV2V and DAIR‑V2X benchmarks, JigsawComm reduces total data volume by over 20‑‑500× while matching or exceeding the accuracy of state‑of‑the‑art methods.

Abstract:
Cooperative perception (CP) offers significant potential to overcome the limitations of single‑vehicle sensing by enabling information sharing among connected vehicles (CVs). However, existing generic CP approaches need to transmit large volumes of perception data that are irrelevant to the driving safety, exceeding available communication bandwidth. Moreover, most CP frameworks rely on pre‑defined communication partners, making them unsuitable for dynamic traffic environments. This paper proposes a Spontaneous Risk‑Aware Selective Cooperative Perception (SRA‑CP) framework to address these challenges. SRA‑CP introduces a decentralized protocol where connected agents continuously broadcast lightweight perception coverage summaries and initiate targeted cooperation only when risk‑relevant blind zones are detected. A perceptual risk identification module enables each CV to locally assess the impact of occlusions on its driving task and determine whether cooperation is necessary. When CP is triggered, the ego vehicle selects appropriate peers based on shared perception coverage and engages in selective information exchange through a fusion module that prioritizes safety‑critical content and adapts to bandwidth constraints. We evaluate SRA‑CP on a public dataset against several representative baselines. Results show that SRA‑CP achieves less than 1% average precision (AP) loss for safety‑critical objects compared to generic CP, while using only 20% of the communication bandwidth. Moreover, it improves the perception performance by 15% over existing selective CP methods that do not incorporate risk awareness.

Abstract:
Multi‑robot systems have emerged as a key technology for addressing the efficiency and cost challenges in labor‑intensive industries. In the representative scenario of smart farming, planning efficient harvesting schedules for a fleet of electric robots presents a highly challenging frontier problem. The complexity arises not only from the need to find Pareto‑optimal solutions for the conflicting objectives of makespan and transportation cost, but also from the necessity to simultaneously manage payload constraints and finite battery capacity. When robot loads are dynamically updated during planned multi‑trip operations, a mandatory recharge triggered by energy constraints introduces an unscheduled load reset. This interaction creates a complex cascading effect that disrupts the entire schedule and renders traditional optimization methods ineffective. To address this challenge, this paper proposes the segment anchoring‑based balancing algorithm (SABA). The core of SABA lies in the organic combination of two synergistic mechanisms: the sequential anchoring and balancing mechanism, which leverages charging decisions as `anchors' to systematically reconstruct disrupted routes, while the proportional splitting‑based rebalancing mechanism is responsible for the fine‑grained balancing and tuning of the final solutions' makespans. Extensive comparative experiments, conducted on a real‑world case study and a suite of benchmark instances, demonstrate that SABA comprehensively outperforms 6 state‑of‑the‑art algorithms in terms of both solution convergence and diversity. This research provides a novel theoretical perspective and an effective solution for the multi‑robot task allocation problem under energy constraints.

Abstract:
Multi‑robot systems in complex physical collaborations face a "shared brain dilemma": transmitting high‑dimensional multimedia data (e.g., video streams at ~30MB/s) creates severe bandwidth bottlenecks and decision‑making latency. To address this, we propose PIPHEN, an innovative distributed physical cognition‑control framework. Its core idea is to replace "raw data communication" with "semantic communication" by performing "semantic distillation" at the robot edge, reconstructing high‑dimensional perceptual data into compact, structured physical representations. This idea is primarily realized through two key components: (1) a novel Physical Interaction Prediction Network (PIPN), derived from large model knowledge distillation, to generate this representation; and (2) a Hamiltonian Energy Network (HEN) controller, based on energy conservation, to precisely translate this representation into coordinated actions. Experiments show that, compared to baseline methods, PIPHEN can compress the information representation to less than 5% of the original data volume and reduce collaborative decision‑making latency from 315ms to 76ms, while significantly improving task success rates. This work provides a fundamentally efficient paradigm for resolving the "shared brain dilemma" in resource‑constrained multi‑robot systems.

Abstract:
Many robots are not equipped with a manipulator and many objects are not suitable for prehensile manipulation (such as large boxes and cylinders). In these cases, pushing is a simple yet effective non‑prehensile skill for robots to interact with and further change the environment. Existing work often assumes a set of predefined pushing modes and fixed‑shape objects. This work tackles the general problem of controlling a robotic fleet to push collaboratively numerous arbitrary objects to respective destinations, within complex environments of cluttered and movable obstacles. It incorporates several characteristic challenges for multi‑robot systems such as online task coordination under large uncertainties of cost and duration, and for contact‑rich tasks such as hybrid switching among different contact modes, and under‑actuation due to constrained contact forces. The proposed method is based on combinatorial hybrid optimization over dynamic task assignments and hybrid execution via sequences of pushing modes and associated forces. It consists of three main components: (I) the decomposition, ordering and rolling assignment of pushing subtasks to robot subgroups; (II) the keyframe guided hybrid search to optimize the sequence of parameterized pushing modes for each subtask; (III) the hybrid control to execute these modes and transit among them. Last but not least, a diffusion‑based accelerator is adopted to predict the keyframes and pushing modes that should be prioritized during hybrid search; and further improve planning efficiency. The framework is complete under mild assumptions. Its efficiency and effectiveness under different numbers of robots and general‑shaped objects are validated extensively in simulations and hardware experiments, as well as generalizations to heterogeneous robots, planar assembly and 6D pushing.

Abstract:
Multi‑robot systems are increasingly being used for critical applications such as rescuing injured people, delivering food and medicines, and monitoring key areas. These applications usually involve navigating at high speeds through constrained spaces such as small gaps. Navigating such constrained spaces becomes particularly challenging when the space is crowded with multiple heterogeneous agents all of which have urgent priorities. What makes the problem even harder is that during an active response situation, roles and priorities can quickly change on a dime without informing the other agents. In order to complete missions in such environments, robots must not only be safe, but also agile, able to dodge and change course at a moment's notice. In this paper, we propose FACA, a fair and agile collision avoidance approach where robots coordinate their tasks by talking to each other via natural language (just as people do). In FACA, robots balance safety with agility via a novel artificial potential field algorithm that creates an automatic ``roundabout'' effect whenever a conflict arises. Our experiments show that FACA achieves a improvement in efficiency, completing missions more than 3.5X faster than baselines with a time reduction of over 70% while maintaining robust safety margins.

Abstract:
Vehicle‑to‑Vehicle (V2V) cooperative perception has great potential to enhance autonomous driving performance by overcoming perception limitations in complex adverse traffic scenarios (CATS). Meanwhile, data serves as the fundamental infrastructure for modern autonomous driving AI. However, due to stringent data collection requirements, existing datasets focus primarily on ordinary traffic scenarios, constraining the benefits of cooperative perception. To address this challenge, we introduce CATS‑V2V, the first‑of‑its‑kind real‑world dataset for V2V cooperative perception under complex adverse traffic scenarios. The dataset was collected by two hardware time‑synchronized vehicles, covering 10 weather and lighting conditions across 10 diverse locations. The 100‑clip dataset includes 60K frames of 10 Hz LiDAR point clouds and 1.26M multi‑view 30 Hz camera images, along with 750K anonymized yet high‑precision RTK‑fixed GNSS and IMU records. Correspondingly, we provide time‑consistent 3D bounding box annotations for objects, as well as static scenes to construct a 4D BEV representation. On this basis, we propose a target‑based temporal alignment method, ensuring that all objects are precisely aligned across all sensor modalities. We hope that CATS‑V2V, the largest‑scale, most supportive, and highest‑quality dataset of its kind to date, will benefit the autonomous driving community in related tasks.

Abstract:
Decentralized collision avoidance is a core challenge for scalable multi‑robot systems. One of the promising approaches to tackle this problem is Model Predictive Path Integral (MPPI) ‑‑ a framework that naturally handles arbitrary motion models and provides strong theoretical guarantees. Still, in practice MPPI‑based controller may provide suboptimal trajectories as its performance relies heavily on uninformed random sampling. In this work, we introduce CoRL‑MPPI, a novel fusion of Cooperative Reinforcement Learning and MPPI to address this limitation. We train an action policy (approximated as deep neural network) in simulation that learns local cooperative collision avoidance behaviors. This learned policy is then embedded into the MPPI framework to guide its sampling distribution, biasing it towards more intelligent and cooperative actions. Notably, CoRL‑MPPI preserves all the theoretical guarantees of regular MPPI. We evaluate our approach in dense, dynamic simulation environments against state‑of‑the‑art baselines, such as ORCA, BVC, RL‑RVO‑NAV and classical MPPI. Our results demonstrate that CoRL‑MPPI significantly improves navigation efficiency (measured by success rate and makespan) and safety, enabling agile and robust multi‑robot navigation.

Abstract:
In the field of swarm robotics, one of the most studied problem is Gathering. It asks for a distributed algorithm that brings the robots to a common location, not known in advance. We consider the case of robots constrained to move along the edges of a graph under the well‑known OBLOT model. Gathering is then accomplished once all the robots occupy a same vertex. Differently from classical settings, we assume: i) the initial configuration may contain multiplicities, i.e. more than one robot may occupy the same vertex; ii) robots cannot detect multiplicities; iii) robots move along the edges of vertex‑ and edge‑transitive graphs, i.e. graphs where all the vertices (and the edges, resp.) belong to a same class of equivalence. To balance somehow such a `hostile' setting, as a scheduler for the activation of the robots, we consider the round‑robin, where robots are cyclically activated one at a time. We provide some basic impossibility results and we design two different algorithms approaching the Gathering for robots moving on two specific topologies belonging to edge‑ and vertex‑transitive graphs: infinite grids and hypercubes. The two algorithms are both time‑optimal and heavily exploit the properties of the underlying topologies. Because of this, we conjecture that no general algorithm can exist for all the solvable cases.

Abstract:
This paper presents a novel approach to avoiding jackknifing and mutual collisions in Heavy Articulated Vehicles (HAVs) by leveraging decentralized swarm intelligence. In contrast to typical swarm robotics research, our robots are elongated and exhibit complex kinematics, introducing unique challenges. Despite its relevance to real‑world applications such as logistics automation, remote mining, airport baggage transport, and agricultural operations, this problem has not been addressed in the existing literature. To tackle this new class of swarm robotics problems, we propose a purely reaction‑based, decentralized swarm intelligence strategy tailored to automate elongated, articulated vehicles. The method presented in this paper prioritizes jackknifing avoidance and establishes a foundation for mutual collision avoidance. We validate our approach through extensive simulation experiments and provide a comprehensive analysis of its performance. For the experiments with a single HAV, we observe that for 99.8% jackknifing was successfully avoided and that 86.7% and 83.4% reach their first and second goals, respectively. With two HAVs interacting, we observe 98.9%, 79.4%, and 65.1%, respectively, while 99.7% of the HAVs do not experience mutual collisions.

Abstract:
Precise underwater positioning remains a fundamental challenge for underwater robotics since global navigation satellite system (GNSS) signals cannot penetrate the sea surface. This paper presents Raspi^2USBL, an open‑source, Raspberry Pi‑based passive inverted ultra‑short baseline (piUSBL) positioning system designed to provide a low‑cost and accessible solution for underwater robotic research. The system comprises a passive acoustic receiver and an active beacon. The receiver adopts a modular hardware architecture that integrates a hydrophone array, a multichannel preamplifier, an oven‑controlled crystal oscillator (OCXO), a Raspberry Pi 5, and an MCC‑series data acquisition (DAQ) board. Apart from the Pi 5, OCXO, and MCC board, the beacon comprises an impedance‑matching network, a power amplifier, and a transmitting transducer. An open‑source C++ software framework provides high‑precision clock synchronization and triggering for one‑way travel‑time (OWTT) messaging, while performing real‑time signal processing, including matched filtering, array beamforming, and adaptive gain control, to estimate the time of flight (TOF) and direction of arrival (DOA) of received signals. The Raspi^2USBL system was experimentally validated in an anechoic tank, freshwater lake, and open‑sea trials. Results demonstrate a slant‑range accuracy better than 0.1%, a bearing accuracy within 0.1^\circ, and stable performance over operational distances up to 1.3 km. These findings confirm that low‑cost, reproducible hardware can deliver research‑grade underwater positioning accuracy. By releasing both the hardware and software as open‑source, Raspi^2USBL provides a unified reference platform that lowers the entry barrier for underwater robotics laboratories, fosters reproducibility, and promotes collaborative innovation in underwater acoustic navigation and swarm robotics.

Abstract:
Biological swarms, such as ant colonies, achieve collective goals through decentralized and stochastic individual behaviors. Similarly, physical systems composed of gases, liquids, and solids exhibit random particle motion governed by entropy maximization, yet do not achieve collective objectives. Despite this analogy, no unified framework exists to explain the stochastic behavior in both biological and physical systems. Here, we present empirical evidence from Formica polyctena ants that reveals a shared statistical mechanism underlying both systems: maximization under different energy function constraints. We further demonstrate that robotic swarms governed by this principle can exhibit scalable, decentralized cooperation, mimicking physical phase‑like behaviors with minimal individual computation. These findings established a unified stochastic model linking biological, physical, and robotic swarms, offering a scalable principle for designing robust and intelligent swarm robotics.

Abstract:
Multi‑agent systems (MAS) are central to applications such as swarm robotics and traffic routing, where agents must coordinate in a decentralized manner to achieve a common objective. Stochastic Shortest Path (SSP) problems provide a natural framework for modeling decentralized control in such settings. While the problem of learning in SSP has been extensively studied in single‑agent settings, the decentralized multi‑agent variant remains largely unexplored. In this work, we take a step towards addressing that gap. We study decentralized multi‑agent SSPs (Dec‑MASSPs) under linear function approximation, where the transition dynamics and costs are represented using linear models. Applying novel symmetry‑based arguments, we identify the structure of optimal policies. Our main contribution is the first regret lower bound for this setting based on the construction of hard‑to‑learn instances for any number of agents, n. Our regret lower bound of Ω(\sqrtK), over K episodes, highlights the inherent learning difficulty in Dec‑MASSPs. These insights clarify the learning complexity of decentralized control and can further guide the design of efficient learning algorithms in multi‑agent systems.

Abstract:
Intelligent transportation systems (ITS) still have a hard time accurately predicting traffic in cities, especially in big, multimodal settings with complicated spatiotemporal dynamics. This paper presents HybridST, a hybrid architecture that integrates Graph Neural Networks (GNNs), multi‑head temporal Transformers, and supervised ensemble learning methods (XGBoost or Random Forest) to collectively capture spatial dependencies, long‑range temporal patterns, and exogenous signals, including weather, calendar, or control states. We test our model on the METR‑LA, PEMS‑BAY, and Seattle Loop tree public benchmark datasets. These datasets include situations ranging from freeway sensor networks to vehicle‑infrastructure cooperative perception. Experimental results show that HybridST consistently beats classical baselines (LSTM, GCN, DCRNN, PDFormer) on important metrics like MAE and RMSE, while still being very scalable and easy to understand. The proposed framework presents a promising avenue for real‑time urban mobility planning, energy optimization, and congestion alleviation strategies, especially within the framework of smart cities and significant events such as the 2030 FIFA World Cup.

Abstract:
Collaborating teams of robots show promise due in their ability to complete missions more efficiently and with improved robustness, attributes that are particularly useful for systems operating in marine environments. A key issue is how to model, analyze, and design these multi‑robot systems to realize the full benefits of collaboration, a challenging task since the domain of multi‑robot autonomy encompasses both collective and individual behaviors. This paper introduces a layered model of multi‑robot autonomy that uses the principle of census, or a weighted count of the inputs from neighbors, for collective decision‑making about teaming, coupled with multi‑objective behavior optimization for individual decision‑making about actions. The census component is expressed as a nonlinear opinion dynamics model and the multi‑objective behavior optimization is accomplished using interval programming. This model can be reduced to recover foundational algorithms in distributed optimization and control, while the full model enables new types of collective behaviors that are useful in real‑world scenarios. To illustrate these points, a new method for distributed optimization of subgroup allocation is introduced where robots use a gradient descent algorithm to minimize portions of the cost functions that are locally known, while being influenced by the opinion states from neighbors to account for the unobserved costs. With this method the group can collectively use the information contained in the Hessian matrix of the total global cost. The utility of this model is experimentally validated in three categorically different experiments with fleets of autonomous surface vehicles: an adaptive sampling scenario, a high value unit protection scenario, and a competitive game of capture the flag.

Abstract:
Multi‑robot systems, particularly mobile manipulators, face challenges in control coordination and dynamic stability when working together. To address this issue, this study proposes MobiDock, a modular self‑reconfigurable mobile manipulator system that allows two independent robots to physically connect and form a unified mobile bimanual platform. This process helps transform a complex multi‑robot control problem into the management of a simpler, single system. The system utilizes an autonomous docking strategy based on computer vision with AprilTag markers and a new threaded screw‑lock mechanism. Experimental results show that the docked configuration demonstrates better performance in dynamic stability and operational efficiency compared to two independently cooperating robots. Specifically, the unified system has lower Root Mean Square (RMS) Acceleration and Jerk values, higher angular precision, and completes tasks significantly faster. These findings confirm that physical reconfiguration is a powerful design principle that simplifies cooperative control, improving stability and performance for complex tasks in real‑world environments.

Abstract:
The European Space Agency (ESA) and the European Space Resources Innovation Centre (ESRIC) created the Space Resources Challenge to invite researchers and companies to propose innovative solutions for Multi‑Robot Systems (MRS) space prospection. This paper proposes the Resilient Exploration And Lunar Mapping System 2 (REALMS2), a MRS framework for planetary prospection and mapping. Based on Robot Operating System version 2 (ROS 2) and enhanced with Visual Simultaneous Localisation And Mapping (vSLAM) for map generation, REALMS2 uses a mesh network for a robust ad hoc network. A single graphical user interface (GUI) controls all the rovers, providing a simple overview of the robotic mission. This system is designed for heterogeneous multi‑robot exploratory missions, tackling the challenges presented by extraterrestrial environments. REALMS2 was used during the second field test of the ESA‑ESRIC Challenge and allowed to map around 60% of the area, using three homogeneous rovers while handling communication delays and blackouts.

Abstract:
Swarms evolving from collective behaviors among multiple individuals are commonly seen in nature, which enables biological systems to exhibit more efficient and robust collaboration. Creating similar swarm intelligence in engineered robots poses challenges to the design of collaborative algorithms that can be programmed at large scales. The assignment‑based method has played an eminent role for a very long time in solving collaboration problems of robot swarms. However, it faces fundamental limitations in terms of efficiency and robustness due to its unscalability to swarm variants. This article presents a tutorial review on recent advances in assignment‑free collaboration of robot swarms, focusing on the problem of shape formation. A key theoretical component is the recently developed \emphmean‑shift exploration strategy, which improves the collaboration efficiency of large‑scale swarms by dozens of times. Further, the efficiency improvement is more significant as the swarm scale increases. Finally, this article discusses three important applications of the mean‑shift exploration strategy, including precise shape formation, area coverage formation, and maneuvering formation, as well as their corresponding industrial scenarios in smart warehousing, area exploration, and cargo transportation.

Abstract:
The Team Orienteering Problem (TOP) generalizes many real‑world multi‑robot scheduling and routing tasks that occur in autonomous mobility, aerial logistics, and surveillance applications. While many flavors of the TOP exist for planning in multi‑robot systems, they assume that all the robots cooperate toward a single objective; thus, they do not extend to settings where the robots compete in reward‑scarce environments. We propose Stochastic Prize‑Collecting Games (SPCG) as an extension of the TOP to plan in the presence of self‑interested robots operating on a graph, under energy constraints and stochastic transitions. A theoretical study on complete and star graphs establishes that there is a unique pure Nash equilibrium in SPCGs that coincides with the optimal routing solution of an equivalent TOP given a rank‑based conflict resolution rule. This work proposes two algorithms: Ordinal Rank Search (ORS) to obtain the ''ordinal rank'' ‑‑one's effective rank in temporarily‑formed local neighborhoods during the games' stages, and Fictitious Ordinal Response Learning (FORL) to obtain best‑response policies against one's senior‑rank opponents. Empirical evaluations conducted on road networks and synthetic graphs under both dynamic and stationary prize distributions show that 1) the state‑aliasing induced by OR‑conditioning enables learning policies that scale more efficiently to large team sizes than those trained with the global index, and 2) Policies trained with FORL generalize better to imbalanced prize distributions than those with other multi‑agent training methods. Finally, the learned policies in the SPCG achieved between 87% and 95% optimality compared to an equivalent TOP solution obtained by mixed‑integer linear programming.

Abstract:
This survey comprehensively reviews the evolving field of multi‑robot collaborative Simultaneous Localization and Mapping (SLAM) using 3D Gaussian Splatting (3DGS). As an explicit scene representation, 3DGS has enabled unprecedented real‑time, high‑fidelity rendering, ideal for robotics. However, its use in multi‑robot systems introduces significant challenges in maintaining global consistency, managing communication, and fusing data from heterogeneous sources. We systematically categorize approaches by their architecture ‑‑ centralized, distributed ‑‑ and analyze core components like multi‑agent consistency and alignment, communication‑efficient, Gaussian representation, semantic distillation, fusion and pose optimization, and real‑time scalability. In addition, a summary of critical datasets and evaluation metrics is provided to contextualize performance. Finally, we identify key open challenges and chart future research directions, including lifelong mapping, semantic association and mapping, multi‑model for robustness, and bridging the Sim2Real gap.

Abstract:
Despite their mechanical sophistication, surgical robots remain blind to their surroundings. This lack of spatial awareness causes collisions, system recoveries, and workflow disruptions, issues that will intensify with the introduction of distributed robots with independent interacting arms. Existing tracking systems rely on bulky infrared cameras and reflective markers, providing only limited views of the surgical scene and adding hardware burden in crowded operating rooms. We present a marker‑free proprioception method that enables precise localisation of surgical robots under their sterile draping despite associated obstruction of visual cues. Our method solely relies on lightweight stereo‑RGB cameras and novel transformer‑based deep learning models. It builds on the largest multi‑centre spatial robotic surgery dataset to date (1.4M self‑annotated images from human cadaveric and preclinical in vivo studies). By tracking the entire robot and surgical scene, rather than individual markers, our approach provides a holistic view robust to occlusions, supporting surgical scene understanding and context‑aware control. We demonstrate an example of potential clinical benefits during in vivo breathing compensation with access to tissue dynamics, unobservable under state of the art tracking, and accurately locate in multi‑robot systems for future intelligent interaction. In addition, and compared with existing systems, our method eliminates markers and improves tracking visibility by 25%. To our knowledge, this is the first demonstration of marker‑free proprioception for fully draped surgical robots, reducing setup complexity, enhancing safety, and paving the way toward modular and autonomous robotic surgery.

Abstract:
Swarm robotics has potential for a wide variety of applications, but real‑world deployments remain rare due to the difficulty of predicting emergent behaviors arising from simple local interactions. Traditional engineering approaches design controllers to achieve desired macroscopic outcomes under idealized conditions, while agent‑based and artificial life studies explore emergent phenomena in a bottom‑up, exploratory manner. In this work, we introduce Analytical Swarm Chemistry, a framework that integrates concepts from engineering, agent‑based and artificial life research, and chemistry. This framework combines macrostate definitions with phase diagram analysis to systematically explore how swarm parameters influence emergent behavior. Inspired by concepts from chemistry, the framework treats parameters like thermodynamic variables, enabling visualization of regions in parameter space that give rise to specific behaviors. Applying this framework to agents with minimally viable capabilities, we identify sufficient conditions for behaviors such as milling and diffusion and uncover regions of the parameter space that reliably produce these behaviors. Preliminary validation on real robots demonstrates that these regions correspond to observable behaviors in practice. By providing a principled, interpretable approach, this framework lays the groundwork for predictable and reliable emergent behavior in real‑world swarm systems.

Abstract:
Pose estimation is essential for many applications within computer vision and robotics. Despite its uses, few works provide rigorous uncertainty quantification for poses under dense or learned models. We derive a closed‑form lower bound on the covariance of camera pose estimates by treating a differentiable renderer as a measurement function. Linearizing image formation with respect to a small pose perturbation on the manifold yields a render‑aware Cramér‑Rao bound. Our approach reduces to classical bundle‑adjustment uncertainty, ensuring continuity with vision theory. It also naturally extends to multi‑agent settings by fusing Fisher information across cameras. Our statistical formulation has downstream applications for tasks such as cooperative perception and novel view synthesis without requiring explicit keypoint correspondences.

Abstract:
In this paper, we introduce a decentralized optimization‑based density controller designed to enforce set invariance constraints in multi‑robot systems. By designing a decentralized control barrier function, we derived sufficient conditions under which local safety constraints guarantee global safety. We account for localization and motion noise explicitly by modeling robots as spatial probability density functions governed by the Fokker‑Planck equation. Compared to traditional centralized approaches, our controller requires less computational and communication power, making it more suitable for deployment in situations where perfect communication and localization are impractical. The controller is validated through simulations and experiments with four quadcopters.

Abstract:
We study the problem of jointly selecting sensing agents and synthesizing decentralized active perception policies for the chosen subset of agents within a Decentralized Partially Observable Markov Decision Process (Dec‑POMDP) framework. Our approach employs a two‑layer optimization structure. In the inner layer, we introduce information‑theoretic metrics, defined by the mutual information between the unknown trajectories or some hidden property in the environment and the collective partial observations in the multi‑agent system, as a unified objective for active perception problems. We employ various optimization methods to obtain optimal sensor policies that maximize mutual information for distinct active perception tasks. In the outer layer, we prove that under certain conditions, the information‑theoretic objectives are monotone and submodular with respect to the subset of observations collected from multiple agents. We then exploit this property to design an IMAS^2 (Information‑theoretic Multi‑Agent Selection and Sensing) algorithm for joint sensing agent selection and sensing policy synthesis. However, since the policy search space is infinite, we adapt the classical Nemhauser‑Wolsey argument to prove that the proposed IMAS^2 algorithm can provide a tight (1 ‑ 1/e)‑guarantee on the performance. Finally, we demonstrate the effectiveness of our approach in a multi‑agent cooperative perception in a grid‑world environment.

Abstract:
We consider Connected Unlabeled Multi‑Agent Pathfinding (CUMAPF), a variant of MAPF where interchangeable agents must be connected at all times. This problem is fundamental to swarm robotics applications such as self‑reconfiguration and marching, where standard MAPF is insufficient as it does not guarantee the connectivity constraint. Despite its simple structure, CUMAPF remains understudied and lacks practical algorithms. We first develop an Integer Linear Programming (ILP) reduction to solve CUMAPF. Although this formulation provides a makespan‑optimal plan, it is severely limited in terms of scalability and real‑time responsiveness due to the large number of variables. We therefore propose a suboptimal but complete algorithm named PULL. It is based on a rule‑based one‑step function that computes a subsequent configuration that preserves connectivity and advances towards the target configuration. PULL is lightweight, and runs in O(n^2) time per step in a 2D grid, where n is the number of agents. Empirically, PULL can quickly solve randomly generated instances containing hundreds of agents, which ILP cannot handle. Furthermore, PULL's solution substantially improves upon a naive approach to CUMAPF.

Abstract:
Imitation Learning (IL) is a natural way for humans to teach robots, particularly when high‑quality demonstrations are easy to obtain. While IL has been widely applied to single‑robot settings, relatively few studies have addressed the extension of these methods to multi‑agent systems, especially in settings where a single human must provide demonstrations to a team of collaborating robots. In this paper, we introduce and study Round‑Robin Behavior Cloning (R2BC), a method that enables a single human operator to effectively train multi‑robot systems through sequential, single‑agent demonstrations. Our approach allows the human to teleoperate one agent at a time and incrementally teach multi‑agent behavior to the entire system, without requiring demonstrations in the joint multi‑agent action space. We show that R2BC methods match, and in some cases surpass, the performance of an oracle behavior cloning approach trained on privileged synchronized demonstrations across four multi‑agent simulated tasks. Finally, we deploy R2BC on two physical robot tasks trained using real human demonstrations.

Abstract:
The reliable execution of high‑level missions in multi‑robot systems with heterogeneous agents, requires robust methods for detecting spurious behaviors. In this paper, we address the challenge of identifying spurious executions of plans specified as a Linear Temporal Logic (LTL) formula, as incorrect task sequences, violations of spatial constraints, timing inconsistencies, or deviations from intended mission semantics. To tackle this, we introduce a structured data generation framework based on the Nets‑within‑Nets (NWN) paradigm, which coordinates robot actions with LTL‑derived global mission specifications. We further propose a Transformer‑based anomaly detection pipeline that classifies robot trajectories as normal or anomalous. Experimental evaluations show that our method achieves high accuracy (91.3%) in identifying execution inefficiencies, and demonstrates robust detection capabilities for core mission violations (88.3%) and constraint‑based adaptive anomalies (66.8%). An ablation experiment of the embedding and architecture was carried out, obtaining successful results where our novel proposition performs better than simpler representations.

Abstract:
We present a minimal model for autonomous robotic swarms in one‑ and higher‑dimensional spaces, where identical, field‑driven agents interact pairwise to self‑organize spacing and independently follow local gradients sensed through quantized digital sensors. We show that the collective response of a multi‑agent train amplifies sensitivity to weak gradients beyond what is achievable by a single agent. We discover a fractional transport phenomenon in which, under a uniform gradient, collective motion freezes abruptly whenever the ratio of intra‑agent sensor separation to inter‑agent spacing satisfies a number‑theoretic commensurability condition. This commensurability locking persists even as the number of agents tends to infinity. We find that this condition is exactly solvable on the rationals ‑‑ a dense subset of real numbers ‑‑ providing analytic, testable predictions for when transport stalls. Our findings establish a surprising bridge between number theory and emergent transport in swarm robotics, informing design principles with implications for collective migration, analog computation, and even the exploration of number‑theoretic structure via physical experimentation.

Abstract:
In this paper, we propose a novel few‑shot learning framework for multi‑robot systems that integrate both spatial and temporal elements: Few‑Shot Demonstration‑Driven Task Coordination and Trajectory Execution (DDACE). Our approach leverages temporal graph networks for learning task‑agnostic temporal sequencing and Gaussian Processes for spatial trajectory modeling, ensuring modularity and generalization across various tasks. By decoupling temporal and spatial aspects, DDACE requires only a small number of demonstrations, significantly reducing data requirements compared to traditional learning from demonstration approaches. To validate our proposed framework, we conducted extensive experiments in task environments designed to assess various aspects of multi‑robot coordination‑such as multi‑sequence execution, multi‑action dynamics, complex trajectory generation, and heterogeneous configurations. The experimental results demonstrate that our approach successfully achieves task execution under few‑shot learning conditions and generalizes effectively across dynamic and diverse settings. This work underscores the potential of modular architectures in enhancing the practicality and scalability of multi‑robot systems in real‑world applications. Additional materials are available at https://sites.google.com/view/ddace.

Abstract:
Recognizing the motion of Micro Aerial Vehicles (MAVs) is crucial for enabling cooperative perception and control in autonomous aerial swarms. Yet, vision‑based recognition models relying only on RGB data often fail to capture the complex spatial temporal characteristics of MAV motion, which limits their ability to distinguish different actions. To overcome this problem, this paper presents MAVR‑Net, a multi‑view learning‑based MAV action recognition framework. Unlike traditional single‑view methods, the proposed approach combines three complementary types of data, including raw RGB frames, optical flow, and segmentation masks, to improve the robustness and accuracy of MAV motion recognition. Specifically, ResNet‑based encoders are used to extract discriminative features from each view, and a multi‑scale feature pyramid is adopted to preserve the spatiotemporal details of MAV motion patterns. To enhance the interaction between different views, a cross‑view attention module is introduced to model the dependencies among various modalities and feature scales. In addition, a multi‑view alignment loss is designed to ensure semantic consistency and strengthen cross‑view feature representations. Experimental results on benchmark MAV action datasets show that our method clearly outperforms existing approaches, achieving 97.8%, 96.5%, and 92.8% accuracy on the Short MAV, Medium MAV, and Long MAV datasets, respectively.

Abstract:
Autonomous Underwater vehicles must operate in strong currents, limited acoustic bandwidth, and persistent sensing requirements where conventional swarm optimisation methods are unreliable. This paper formulates an irreversible hydrodynamic deployment problem for Autonomous Underwater Vehicle (AUV) swarms and presents Nauplius Optimisation for Autonomous Hydrodynamics (NOAH), a novel nature‑inspired swarm optimisation algorithm that combines current‑aware drift, irreversible settlement in persistent sensing nodes, and colony‑based communication. Drawing inspiration from the behaviour of barnacle nauplii, NOAH addresses the critical limitations of existing swarm algorithms by providing hydrodynamic awareness, irreversible anchoring mechanisms, and colony‑based communication capabilities essential for underwater exploration missions. The algorithm establishes a comprehensive foundation for scalable and energy‑efficient underwater swarm robotics with validated performance analysis. Validation studies demonstrate an 86% success rate for permanent anchoring scenarios, providing a unified formulation for hydrodynamic constraints and irreversible settlement behaviours with an empirical study under flow.

Abstract:
We introduce a novel framework for automatic behavior tree (BT) construction in heterogeneous multi‑robot systems, designed to address the challenges of adaptability and robustness in dynamic environments. Traditional robots are limited by fixed functional attributes and cannot efficiently reconfigure their strategies in response to task failures or environmental changes. To overcome this limitation, we leverage large language models (LLMs) to generate and extend BTs dynamically, combining the reasoning and generalization power of LLMs with the modularity and recovery capability of BTs. The proposed framework consists of four interconnected modules task initialization, task assignment, BT update, and failure node detection which operate in a closed loop. Robots tick their BTs during execution, and upon encountering a failure node, they can either extend the tree locally or invoke a centralized virtual coordinator (Alex) to reassign subtasks and synchronize BTs across peers. This design enables long‑term cooperative execution in heterogeneous teams. We validate the framework on 60 tasks across three simulated scenarios and in a real‑world cafe environment with a robotic arm and a wheeled‑legged robot. Results show that our method consistently outperforms baseline approaches in task success rate, robustness, and scalability, demonstrating its effectiveness for multi‑robot collaboration in complex scenarios.

Abstract:
Jumping is an efficient locomotion strategy to traverse cluttered, uneven, or unstable environments in nature, yet replicating continuous, autonomous leaping in soft robots remains challenging due to limited energy storage and reliance on human intervention or latches. Here, we report a millimeter‑scale, self‑resetting soft ring that achieves repeated vertical and stable horizontal leaps under uniform infrared illumination without external control. The ring‑shaped liquid crystal elastomer body twists to store elastic energy, which is suddenly released when a rigid tail strikes the ground, propelling the robot. During the airborne phase, the twisted body autonomously untwists, resetting for the next cycle. By tuning geometric asymmetry and the center of mass, the robot transitions between crawling, directional leaping, and vertical jumping. Optimized configurations yield vertical jumps exceeding 80 body heights and directional horizontal leaps over 3 body lengths. Beyond controlled motion on flat ground, the robot demonstrates resilient and robust locomotion across slopes, parallel hurdles, and diverse cluttered natural terrains including grass, wet sand, and mulch. This work establishes a new paradigm of twisting‑enabled, photothermally powered soft robots capable of autonomous, continuous leaping, with potential applications in environmental navigation, swarm robotics, and unstructured terrain navigation.

Abstract:
Our recently introduced self‑organizing nervous system (SoNS) provides robot swarms with 1) ease of behavior design and 2) global estimation of the swarm configuration and its collective environment, facilitating the implementation of online automatic code generation for robot swarms. In a demonstration with 6 real robots and simulation trials with >30 robots, we show that when a SoNS‑enhanced robot swarm gets stuck, it can automatically solicit and run code generated by an external LLM on the fly, completing its mission with an 85% success rate.

Abstract:
Autonomous exploration and object search in unknown indoor environments remain challenging for multi‑robot systems (MRS). Traditional approaches often rely on greedy frontier assignment strategies with limited inter‑robot coordination. In this work, we present Coordinated Multi‑Robot Exploration and Search using Vision Language Models (COMRES‑VLM), a novel framework that leverages Vision Language Models (VLMs) for intelligent coordination of MRS tasked with efficient exploration and target object search. COMRES‑VLM integrates real‑time frontier cluster extraction and topological skeleton analysis with VLM reasoning over shared occupancy maps, robot states, and optional natural language priors, in order to generate globally consistent waypoint assignments. Extensive experiments in large‑scale simulated indoor environments with up to six robots demonstrate that COMRES‑VLM consistently outperforms state‑of‑the‑art coordination methods, including Capacitated Vehicle Routing Problem (CVRP) and Voronoi‑based planners, achieving 10.2% faster exploration completion and 55.7% higher object search efficiency. Notably, COMRES‑VLM enables natural language‑based object search capabilities, allowing human operators to provide high‑level semantic guidance that traditional algorithms cannot interpret.

Abstract:
Learning control policies for multi‑robot systems (MRS) remains a major challenge due to long‑term coordination and the difficulty of obtaining realistic training data. In this work, we address both limitations within an imitation learning framework. First, we shift the typical role of Curriculum Learning in MRS, from scalability with the number of robots, to focus on improving long‑term coordination. We propose a curriculum strategy that gradually increases the length of expert trajectories during training, stabilizing learning and enhancing the accuracy of long‑term behaviors. Second, we introduce a method to approximate the egocentric perception of each robot using only third‑person global state demonstrations. Our approach transforms idealized trajectories into locally available observations by filtering neighbors, converting reference frames, and simulating onboard sensor variability. Both contributions are integrated into a physics‑informed technique to produce scalable, distributed policies from observations. We conduct experiments across two tasks with varying team sizes and noise levels. Results show that our curriculum improves long‑term accuracy, while our perceptual estimation method yields policies that are robust to realistic uncertainty. Together, these strategies enable the learning of robust, distributed controllers from global demonstrations, even in the absence of expert actions or onboard measurements.

Abstract:
With the tremendous advancement of deep learning and communication technology, Vehicle‑to‑Everything (V2X) cooperative perception has the potential to address limitations in sensing distant objects and occlusion for a single‑agent perception system. V2X cooperative perception systems are software systems characterized by diverse sensor types and cooperative agents, varying fusion schemes, and operation under different communication conditions. Therefore, their complex composition gives rise to numerous operational challenges. Furthermore, when cooperative perception systems produce erroneous predictions, the types of errors and their underlying causes remain insufficiently explored. To bridge this gap, we take an initial step by conducting an empirical study of V2X cooperative perception. To systematically evaluate the impact of cooperative perception on the ego vehicle's perception performance, we identify and analyze six prevalent error patterns in cooperative perception systems. We further conduct a systematic evaluation of the critical components of these systems through our large‑scale study and identify the following key findings: (1) The LiDAR‑based cooperation configuration exhibits the highest perception performance; (2) Vehicle‑to‑infrastructure (V2I) and vehicle‑to‑vehicle (V2V) communication exhibit distinct cooperative perception performance under different fusion schemes; (3) Increased cooperative perception errors may result in a higher frequency of driving violations; (4) Cooperative perception systems are not robust against communication interference when running online. Our results reveal potential risks and vulnerabilities in critical components of cooperative perception systems. We hope that our findings can better promote the design and repair of cooperative perception systems.

Abstract:
Cooperative perception enabled by Vehicle‑to‑Everything communication has shown great promise in enhancing situational awareness for autonomous vehicles and other mobile robotic platforms. Despite recent advances in perception backbones and multi‑agent fusion, real‑world deployments remain challenged by hard detection cases, exemplified by partial detections and noise accumulation which limit downstream detection accuracy. This work presents Diffusion on Reinforced Cooperative Perception (DRCP), a real‑time deployable framework designed to address aforementioned issues in dynamic driving environments. DRCP integrates two key components: (1) Precise‑Pyramid‑Cross‑Modality‑Cross‑Agent, a cross‑modal cooperative perception module that leverages camera‑intrinsic‑aware angular partitioning for attention‑based fusion and adaptive convolution to better exploit external features; and (2) Mask‑Diffusion‑Mask‑Aggregation, a novel lightweight diffusion‑based refinement module that encourages robustness against feature perturbations and aligns bird's‑eye‑view features closer to the task‑optimal manifold. The proposed system achieves real‑time performance on mobile platforms while significantly improving robustness under challenging conditions. Code will be released in late 2025.

Abstract:
Multi‑robot Coverage Path Planning (MCPP) addresses the problem of computing paths for multiple robots to effectively cover a large area of interest. Conventional approaches to MCPP typically assume that robots move at fixed velocities, which is often unrealistic in real‑world applications where robots must adapt their speeds based on the specific coverage tasks assigned to them.Consequently, conventional approaches often lead to imbalanced workload distribution among robots and increased completion time for coverage tasks. To address this, we introduce a novel Multi‑robot Dynamic Coverage Path Planning (MDCPP) algorithm for complete coverage in two‑dimensional environments. MDCPP dynamically estimates each robot's remaining workload by approximating the target distribution with Gaussian mixture models, and assigns coverage regions using a capacity‑constrained Voronoi diagram. We further develop a distributed implementation of MDCPP for range‑constrained robotic networks. Simulation results validate the efficacy of MDCPP, showing qualitative improvements and superior performance compared to an existing sweeping algorithm, and a quantifiable impact of communication range on coverage efficiency.

Abstract:
Allocating tasks to heterogeneous robot teams in environments with uncertain task requirements is a fundamentally challenging problem. Redundantly assigning multiple robots to such tasks is overly conservative, while purely reactive strategies risk costly delays in task completion when the uncertain capabilities become necessary. This paper introduces an auction‑based task allocation algorithm that explicitly models uncertain task requirements, leveraging a novel strongly coupled formulation to allocate tasks such that robots with potentially required capabilities are naturally positioned near uncertain tasks. This approach enables robots to remain productive on nearby tasks while simultaneously mitigating large delays in completion time when their capabilities are required. Through a set of simulated disaster relief missions with task deadline constraints, we demonstrate that the proposed approach yields up to a 15% increase in expected mission value compared to redundancy‑based methods. Furthermore, we propose a novel framework to approximate uncertainty arising from unmodeled changes in task requirements by leveraging the natural delay between encountering unexpected environmental conditions and confirming whether additional capabilities are required to complete a task. We show that our approach achieves up to an 18% increase in expected mission value using this framework compared to reactive methods that don't leverage this delay.

Abstract:
Multi‑robot target tracking is a fundamental problem that requires coordinated monitoring of dynamic entities in applications such as precision agriculture, environmental monitoring, disaster response, and security surveillance. While Federated Learning (FL) has the potential to enhance learning across multiple robots without centralized data aggregation, its use in multi‑Unmanned Aerial Vehicle (UAV) target tracking remains largely underexplored. Key challenges include limited onboard computational resources, significant data heterogeneity in FL due to varying targets and the fields of view, and the need for tight coupling between trajectory prediction and multi‑robot planning. In this paper, we introduce DroneFL, the first federated learning framework specifically designed for efficient multi‑UAV target tracking. We design a lightweight local model to predict target trajectories from sensor inputs, using a frozen YOLO backbone and a shallow transformer for efficient onboard training. The updated models are periodically aggregated in the cloud for global knowledge sharing. To alleviate the data heterogeneity that hinders FL convergence, DroneFL introduces a position‑invariant model architecture with altitude‑based adaptive instance normalization. Finally, we fuse predictions from multiple UAVs in the cloud and generate optimal trajectories that balance target prediction accuracy and overall tracking performance. Our results show that DroneFL reduces prediction error by 6%‑83% and tracking distance by 0.4%‑4.6% compared to a distributed non‑FL framework. In terms of efficiency, DroneFL runs in real time on a Raspberry Pi 5 and has on average just 1.56 KBps data rate to the cloud.

Abstract:
Research on lane change prediction has gained attention in the last few years. Most existing works in this area have been conducted in simulation environments or with pre‑recorded datasets, these works often rely on simplified assumptions about sensing, communication, and traffic behavior that do not always hold in practice. Real‑world deployments of lane‑change prediction systems are relatively rare, and when they are reported, the practical challenges, limitations, and lessons learned are often under‑documented. This study explores cooperative lane‑change prediction through a real hardware deployment in mixed traffic and shares the insights that emerged during implementation and testing. We highlight the practical challenges we faced, including bottlenecks, reliability issues, and operational constraints that shaped the behavior of the system. By documenting these experiences, the study provides guidance for others working on similar pipelines.

Abstract:
Swarm intelligence emerges from decentralised interactions among simple agents, enabling collective problem‑solving. This study establishes a theoretical equivalence between pheromone‑mediated aggregation in \celeg\ and reinforcement learning (RL), demonstrating how stigmergic signals function as distributed reward mechanisms. We model engineered nematode swarms performing foraging tasks, showing that pheromone dynamics mathematically mirror cross‑learning updates, a fundamental RL algorithm. Experimental validation with data from literature confirms that our model accurately replicates empirical \celeg\ foraging patterns under static conditions. In dynamic environments, persistent pheromone trails create positive feedback loops that hinder adaptation by locking swarms into obsolete choices. Through computational experiments in multi‑armed bandit scenarios, we reveal that introducing a minority of exploratory agents insensitive to pheromones restores collective plasticity, enabling rapid task switching. This behavioural heterogeneity balances exploration‑exploitation trade‑offs, implementing swarm‑level extinction of outdated strategies. Our results demonstrate that stigmergic systems inherently encode distributed RL processes, where environmental signals act as external memory for collective credit assignment. By bridging synthetic biology with swarm robotics, this work advances programmable living systems capable of resilient decision‑making in volatile environments.

Abstract:
A central challenge for multi‑robot systems is fusing independently gathered perception data into a unified representation. Despite progress in Collaborative SLAM (C‑SLAM), benchmarking remains hindered by the scarcity of dedicated multi‑robot datasets. Many evaluations instead partition single‑robot trajectories, a practice that may only partially reflect true multi‑robot operations and, more critically, lacks standardization, leading to results that are difficult to interpret or compare across studies. While several multi‑robot datasets have recently been introduced, they mostly contain short trajectories with limited inter‑robot overlap and sparse intra‑robot loop closures. To overcome these limitations, we introduce CU‑Multi, a dataset collected over multiple days at two large outdoor sites on the University of Colorado Boulder campus. CU‑Multi comprises four synchronized runs with aligned start times and controlled trajectory overlap, replicating the distinct perspectives of a robot team. It includes RGB‑D sensing, RTK GPS, semantic LiDAR, and refined ground‑truth odometry. By combining overlap variation with dense semantic annotations, CU‑Multi provides a strong foundation for reproducible evaluation in multi‑robot collaborative perception tasks.

Abstract:
In this paper, we present a receding‑horizon, sampling‑based planner capable of reasoning over multimodal policy distributions. By using the cross‑entropy method to optimize a multimodal policy under a common cost function, our approach increases robustness against local minima and promotes effective exploration of the solution space. We show that our approach naturally extends to multi‑robot collision‑free planning, enables agents to share diverse candidate policies to avoid deadlocks, and allows teams to minimize a global objective without incurring the computational complexity of centralized optimization. Numerical simulations demonstrate that employing multiple modes significantly improves success rates in trap environments and in multi‑robot collision avoidance. Hardware experiments further validate the approach's real‑time feasibility and practical performance.

Abstract:
This article presents a novel multi‑agent spatial transformer (MAST) for learning communication policies in large‑scale decentralized and collaborative multi‑robot systems (DC‑MRS). Challenges in collaboration in DC‑MRS arise from: (i) partial observable states as robots make only localized perception, (ii) limited communication range with no central server, and (iii) independent execution of actions. The robots need to optimize a common task‑specific objective, which, under the restricted setting, must be done using a communication policy that exhibits the desired collaborative behavior. The proposed MAST is a decentralized transformer architecture that learns communication policies to compute abstract information to be shared with other agents and processes the received information with the robot's own observations. The MAST extends the standard transformer with new positional encoding strategies and attention operations that employ windowing to limit the receptive field for MRS. These are designed for local computation, shift‑equivariance, and permutation equivariance, making it a promising approach for DC‑MRS. We demonstrate the efficacy of MAST on decentralized assignment and navigation (DAN) and decentralized coverage control. Efficiently trained using imitation learning in a centralized setting, the decentralized MAST policy is robust to communication delays, scales to large teams, and performs better than the baselines and other learning‑based approaches.

Abstract:
The growing global demand for critical raw materials (CRMs) has highlighted the need to access difficult and hazardous environments such as abandoned underground mines. These sites pose significant challenges for conventional machinery and human operators due to confined spaces, structural instability, and lack of infrastructure. To address this, we propose a modular multi‑robot system designed for autonomous operation in such environments, enabling sequential mineral extraction tasks. Unlike existing work that focuses primarily on mapping and inspection through global behavior or central control, our approach incorporates physical interaction capabilities using specialized robots coordinated through local high‑level behavior control. Our proposed system utilizes Hierarchical Finite State Machine (HFSM) behaviors to structure complex task execution across heterogeneous robotic platforms. Each robot has its own HFSM behavior to perform sequential autonomy while maintaining overall system coordination, achieved by triggering behavior execution through inter‑robot communication. This architecture effectively integrates software and hardware components to support collaborative, task‑driven multi‑robot operation in confined underground environments.

Abstract:
Fleets of autonomous robots have been deployed for exploration of unknown scenes for features of interest, e.g., subterranean exploration, reconnaissance, search and rescue missions. During exploration, the robots may encounter un‑identified targets, blocked passages, interactive objects, temporary failure, or other unexpected events, all of which require consistent human assistance with reliable communication for a time period. This however can be particularly challenging if the communication among the robots is severely restricted to only close‑range exchange via ad‑hoc networks, especially in extreme environments like caves and underground tunnels. This paper presents a novel human‑centric interactive exploration and assistance framework called FlyKites, for multi‑robot systems under limited communication. It consists of three interleaved components: (I) the distributed exploration and intermittent communication (called the "spread mode"), where the robots collaboratively explore the environment and exchange local data among the fleet and with the operator; (II) the simultaneous optimization of the relay topology, the operator path, and the assignment of robots to relay roles (called the "relay mode"), such that all requested assistance can be provided with minimum delay; (III) the human‑in‑the‑loop online execution, where the robots switch between different roles and interact with the operator adaptively. Extensive human‑in‑the‑loop simulations and hardware experiments are performed over numerous challenging scenes.

Abstract:
This paper develops a distributed Nash Equilibrium seeking algorithm for heterogeneous multi‑robot systems. The algorithm utilises distributed optimisation and output control to achieve the Nash equilibrium by leveraging information shared among neighbouring robots. Specifically, we propose a distributed optimisation algorithm that calculates the Nash equilibrium as a tailored reference for each robot and designs output control laws for heterogeneous multi‑robot systems to track it in an aggregative game. We prove that our algorithm is guaranteed to converge and result in efficient outcomes. The effectiveness of our approach is demonstrated through numerical simulations and empirical testing with physical robots.

Abstract:
We propose a new formulation for the multi‑robot task allocation problem that incorporates (a) complex precedence relationships between tasks, (b) efficient intra‑task coordination, and (c) cooperation through the formation of robot coalitions. A task graph specifies the tasks and their relationships, and a set of reward functions models the effects of coalition size and preceding task performance. Maximizing task rewards is NP‑hard; hence, we propose network flow‑based algorithms to approximate solutions efficiently. A novel online algorithm performs iterative re‑allocation, providing robustness to task failures and model inaccuracies to achieve higher performance than offline approaches. We comprehensively evaluate the algorithms in a testbed with random missions and reward functions and compare them to a mixed‑integer solver and a greedy heuristic. Additionally, we validate the overall approach in an advanced simulator, modeling reward functions based on realistic physical phenomena and executing the tasks with realistic robot dynamics. Results establish efficacy in modeling complex missions and efficiency in generating high‑fidelity task plans while leveraging task relationships.

Abstract:
The increasing labor costs in agriculture have accelerated the adoption of multi‑robot systems for orchard harvesting. However, efficiently coordinating these systems is challenging due to the complex interplay between makespan and energy consumption, particularly under practical constraints like load‑dependent speed variations and battery limitations. This paper defines the multi‑objective agricultural multi‑electrical‑robot task allocation (AMERTA) problem, which systematically incorporates these often‑overlooked real‑world constraints. To address this problem, we propose a hybrid hierarchical route reconstruction algorithm (HRRA) that integrates several innovative mechanisms, including a hierarchical encoding structure, a dual‑phase initialization method, task sequence optimizers, and specialized route reconstruction operators. Extensive experiments on 45 test instances demonstrate HRRA's superior performance against seven state‑of‑the‑art algorithms. Statistical analysis, including the Wilcoxon signed‑rank and Friedman tests, empirically validates HRRA's competitiveness and its unique ability to explore previously inaccessible regions of the solution space. In general, this research contributes to the theoretical understanding of multi‑robot coordination by offering a novel problem formulation and an effective algorithm, thereby also providing practical insights for agricultural automation.

Abstract:
Pogobots are a new type of open‑source/open‑hardware robots specifically designed for swarm robotics research. Their cost‑effective and modular design, complemented by vibration‑based and wheel‑based locomotion, fast infrared communication and extensive software architecture facilitate the implementation of swarm intelligence algorithms. However, testing even simple distributed algorithms directly on robots is particularly labor‑intensive. Scaling to more complex problems or calibrate user code parameters will have a prohibitively high strain on available resources. In this article we present Pogosim, a fast and scalable simulator for Pogobots, designed to reduce as much as possible algorithm development costs. The exact same code will be used in both simulation and to experimentally drive real robots. This article details the software architecture of Pogosim, explain how to write configuration files and user programs and how simulations approximate or differ from experiments. We describe how a large set of simulations can be launched in parallel, how to retrieve and analyze the simulation results, and how to optimize user code parameters using optimization algorithms.

Abstract:
In robotic systems, the performance of reinforcement learning depends on the rationality of predefined reward functions. However, manually designed reward functions often lead to policy failures due to inaccuracies. Inverse Reinforcement Learning (IRL) addresses this problem by inferring implicit reward functions from expert demonstrations. Nevertheless, existing methods rely heavily on large amounts of expert demonstrations to accurately recover the reward function. The high cost of collecting expert demonstrations in robotic applications, particularly in multi‑robot systems, severely hinders the practical deployment of IRL. Consequently, improving sample efficiency has emerged as a critical challenge in multi‑agent inverse reinforcement learning (MIRL). Inspired by the symmetry inherent in multi‑agent systems, this work theoretically demonstrates that leveraging symmetry enables the recovery of more accurate reward functions. Building upon this insight, we propose a universal framework that integrates symmetry into existing multi‑agent adversarial IRL algorithms, thereby significantly enhancing sample efficiency. Experimental results from multiple challenging tasks have demonstrated the effectiveness of this framework. Further validation in physical multi‑robot systems has shown the practicality of our method.

Abstract:
This paper proposes a framework for multi‑robot systems to perform simultaneous learning and coverage of a domain of interest characterized by an unknown and potentially time‑varying density function. To overcome the limitations of Gaussian Process (GP) regression, we employ Random Feature GP (RFGP) and its online variant (O‑RFGP) which enables online and incremental inference. By integrating these with Voronoi‑based coverage control and Upper Confidence Bound (UCB) sampling strategy, a team of robots can adaptively focus on important regions while refining the learned spatial field for efficient coverage. The incremental update mechanism of O‑RFGP naturally supports time‑varying environments, allowing efficient adaptation without retaining historical data. Furthermore, to the best of our knowledge, we provide the first theoretical analysis of online learning and coverage through a regret‑based formulation, establishing asymptotic no‑regret guarantees in the time‑invariant setting. The effectiveness of the proposed framework is demonstrated through simulations with both time‑invariant and time‑varying density functions, along with a physical experiment with a time‑varying density function.

Abstract:
Micro Autonomous Surface Vehicles (MicroASVs) offer significant potential for operations in confined or shallow waters and swarm robotics applications. However, achieving precise and robust control at such small scales remains highly challenging, mainly due to the complexity of modeling nonlinear hydrodynamic forces and the increased sensitivity to self‑motion effects and environmental disturbances, including waves and boundary effects in confined spaces. This paper presents a physics‑driven dynamics model for an over‑actuated MicroASV and introduces a data‑driven optimal control framework that leverages a weak formulation‑based online model learning method. Our approach continuously refines the physics‑driven model in real time, enabling adaptive control that adjusts to changing system parameters. Simulation results demonstrate that the proposed method substantially enhances trajectory tracking accuracy and robustness, even under unknown payloads and external disturbances. These findings highlight the potential of data‑driven online learning‑based optimal control to improve MicroASV performance, paving the way for more reliable and precise autonomous surface vehicle operations.

Abstract:
The OBLOT model has been extensively studied in theoretical swarm robotics. It assumes weak capabilities for the involved mobile robots, such as they are anonymous, disoriented, no memory of past events (oblivious), and silent. Their only means of (implicit) communication is transferred to their positioning, i.e., stigmergic information. These limited capabilities make the design of distributed algorithms a challenging task. Over the last two decades, numerous research papers have addressed the question of which tasks can be accomplished within this model. Nevertheless, as it usually happens in distributed computing, also in OBLOT the computational power available to the robots is neglected as the main cost measures for the designed algorithms refer to the number of movements or the number of rounds required. In this paper, we prove that for synchronous robots moving on finite graphs, the unlimited computational power (other than finite time) has a significant impact. In fact, by exploiting it, we provide a definitive resolution algorithm that applies to a wide class of problems while guaranteeing the minimum number of moves and rounds.

Abstract:
In comparison with existing approaches, which struggle with scalability, communication dependency, and robustness against dynamic failures, cooperative aerial transportation via robot swarms holds transformative potential for logistics and disaster response. Here, we present a physics‑inspired cooperative transportation approach for flying robot swarms that imitates the dissipative mechanics of table‑leg load distribution. By developing a decentralized dissipative force model, our approach enables autonomous formation stabilization and adaptive load allocation without the requirement of explicit communication. Based on local neighbor robots and the suspended payload, each robot dynamically adjusts its position. This is similar to energy‑dissipating table leg reactions. The stability of the resultant control system is rigorously proved. Simulations demonstrate that the tracking errors of the proposed approach are 20%, 68%, 55.5%, and 21.9% of existing approaches under the cases of capability variation, cable uncertainty, limited vision, and payload variation, respectively. In real‑world experiments with six flying robots, the cooperative aerial transportation system achieved a 94% success rate under single‑robot failure, disconnection events, 25% payload variation, and 40% cable length uncertainty, demonstrating strong robustness under outdoor winds up to Beaufort scale 4. Overall, this physics‑inspired approach bridges swarm intelligence and mechanical stability principles, offering a scalable framework for heterogeneous aerial systems to collectively handle complex transportation tasks in communication‑constrained environments.

Abstract:
To support cooperative perception (CP) of networked mobile agents in dynamic scenarios, the efficient and robust transmission of sensory data is a critical challenge. Deep learning‑based joint source‑channel coding (JSCC) has demonstrated promising results for image transmission under adverse channel conditions, outperforming traditional rule‑based codecs. While recent works have explored to combine JSCC with the widely adopted multiple‑input multiple‑output (MIMO) technology, these approaches are still limited to the discrete‑time analog transmission (DTAT) model and simple tasks. Given the limited performance of existing MIMO JSCC schemes in supporting complex CP tasks for networked mobile agents with digital MIMO communication systems, this paper presents a Synesthesia of Machines (SoM)‑based task‑driven MIMO system for image transmission, referred to as SoM‑MIMO. By leveraging the structural properties of the feature pyramid for perceptual tasks and the channel properties of the closed‑loop MIMO communication system, SoM‑MIMO enables efficient and robust digital MIMO transmission of images. Experimental results have shown that compared with two JSCC baseline schemes, our approach achieves average mAP improvements of 6.30 and 10.48 across all SNR levels, while maintaining identical communication overhead.

Abstract:
In this paper, we propose a novel methodology for path planning and scheduling for multi‑robot navigation that is based on optimal transport theory and model predictive control. We consider a setup where N robots are tasked to navigate to M targets in a common space with obstacles. Mapping robots to targets first and then planning paths can result in overlapping paths that lead to deadlocks. We derive a strategy based on optimal transport that not only provides minimum cost paths from robots to targets but also guarantees non‑overlapping trajectories. We achieve this by discretizing the space of interest into K cells and by imposing a K× K cost structure that describes the cost of transitioning from one cell to another. Optimal transport then provides optimal and non‑overlapping cell transitions for the robots to reach the targets that can be readily deployed without any scheduling considerations. The proposed solution requires \unicodex1D4AA(K^3\log K) computations in the worst‑case and \unicodex1D4AA(K^2\log K) for well‑behaved problems. To further accommodate potentially overlapping trajectories (unavoidable in certain situations) as well as robot dynamics, we show that a temporal structure can be integrated into optimal transport with the help of replans and model predictive control.

Abstract:
Collaborative learning enhances the performance and adaptability of multi‑robot systems in complex tasks but faces significant challenges due to high communication overhead and data heterogeneity inherent in multi‑robot tasks. To this end, we propose CoCoL, a Communication efficient decentralized Collaborative Learning method tailored for multi‑robot systems with heterogeneous local datasets. Leveraging a mirror descent framework, CoCoL achieves remarkable communication efficiency with approximate Newton‑type updates by capturing the similarity between objective functions of robots, and reduces computational costs through inexact sub‑problem solutions. Furthermore, the integration of a gradient tracking scheme ensures its robustness against data heterogeneity. Experimental results on three representative multi robot collaborative learning tasks show the superiority of the proposed CoCoL in significantly reducing both the number of communication rounds and total bandwidth consumption while maintaining state‑of‑the‑art accuracy. These benefits are particularly evident in challenging scenarios involving non‑IID (non‑independent and identically distributed) data distribution, streaming data, and time‑varying network topologies.

Abstract:
Multi‑robot systems are increasingly deployed in applications, such as intralogistics or autonomous delivery, where multiple robots collaborate to complete tasks efficiently. One of the key factors enabling their efficient cooperation is Multi‑Robot Task Allocation (MRTA). Algorithms solving this problem optimize task distribution among robots to minimize the overall execution time. In shared environments, apart from the relative distance between the robots and the tasks, the execution time is also significantly impacted by the delay caused by navigating around moving people. However, most existing MRTA approaches are dynamics‑agnostic, relying on static maps and neglecting human motion patterns, leading to inefficiencies and delays. In this paper, we introduce \acrfullmethod name. This method leverages Maps of Dynamics (MoDs), spatio‑temporal queryable models designed to capture historical human movement patterns, to estimate the impact of humans on the task execution time during deployment. \acrshortmethod name utilizes a stochastic cost function that includes MoDs. Experimental results show that integrating MoDs enhances task allocation performance, resulting in reduced mission completion times by up to 26% compared to the dynamics‑agnostic method and up to 19% compared to the baseline. This work underscores the importance of considering human dynamics in MRTA within shared environments and presents an efficient framework for deploying multi‑robot systems in environments populated by humans.

Abstract:
Robot swarms require cohesive collective behaviour to address diverse challenges, including shape formation and decision‑making. Existing approaches often treat consensus in discrete and continuous decision spaces as distinct problems. We present DANCeRS, a unified, distributed algorithm leveraging Gaussian Belief Propagation (GBP) to achieve consensus in both domains. By representing a swarm as a factor graph our method ensures scalability and robustness in dynamic environments, relying on purely peer‑to‑peer message passing. We demonstrate the effectiveness of our general framework through two applications where agents in a swarm must achieve consensus on global behaviour whilst relying on local communication. In the first, robots must perform path planning and collision avoidance to create shape formations. In the second, we show how the same framework can be used by a group of robots to form a consensus over a set of discrete decisions. Experimental results highlight our method's scalability and efficiency compared to recent approaches to these problems making it a promising solution for multi‑robot systems requiring distributed consensus. We encourage the reader to see the supplementary video demo.

Abstract:
This paper develops a novel COllaborative‑Online‑Learning (COOL)‑enabled motion control framework for multi‑robot systems to avoid collision amid randomly moving obstacles whose motion distributions are partially observable through decentralized data streams. To address the notable challenge of data acquisition due to occlusion, a COOL approach based on the Dirichlet process mixture model is proposed to efficiently extract motion distribution information by exchanging among robots selected learning structures. By leveraging the fine‑grained local‑moment information learned through COOL, a data‑stream‑driven ambiguity set for obstacle motion is constructed. We then introduce a novel ambiguity set propagation method, which theoretically admits the derivation of the ambiguity sets for obstacle positions over the entire prediction horizon by utilizing obstacle current positions and the ambiguity set for obstacle motion. Additionally, we develop a compression scheme with its safety guarantee to automatically adjust the complexity and granularity of the ambiguity set by aggregating basic ambiguity sets that are close in a measure space, thereby striking an attractive trade‑off between control performance and computation time. Then the probabilistic collision‑free trajectories are generated through distributionally robust optimization problems. The distributionally robust obstacle avoidance constraints based on the compressed ambiguity set are equivalently reformulated by deriving separating hyperplanes through tractable semi‑definite programming. Finally, we establish the probabilistic collision avoidance guarantee and the long‑term tracking performance guarantee for the proposed framework. The numerical simulations are used to demonstrate the efficacy and superiority of the proposed approach compared with state‑of‑the‑art methods.

Abstract:
Automotive FMCW radars remain reliable in rain and glare, yet their sparse, noisy point clouds constrain 3‑D object detection. We therefore release CoVeRaP, a 21 k‑frame cooperative dataset that time‑aligns radar, camera, and GPS streams from multiple vehicles across diverse manoeuvres. Built on this data, we propose a unified cooperative‑perception framework with middle‑ and late‑fusion options. Its baseline network employs a multi‑branch PointNet‑style encoder enhanced with self‑attention to fuse spatial, Doppler, and intensity cues into a common latent space, which a decoder converts into 3‑D bounding boxes and per‑point depth confidence. Experiments show that middle fusion with intensity encoding boosts mean Average Precision by up to 9x at IoU 0.9 and consistently outperforms single‑vehicle baselines. CoVeRaP thus establishes the first reproducible benchmark for multi‑vehicle FMCW‑radar perception and demonstrates that affordable radar sharing markedly improves detection robustness. Dataset and code are publicly available to encourage further research.

Abstract:
Extracting narrow roads from high‑resolution remote sensing imagery remains a significant challenge due to their limited width, fragmented topology, and frequent occlusions. To address these issues, we propose D3FNet, a Dilated Dual‑Stream Differential Attention Fusion Network designed for fine‑grained road structure segmentation in remote perception systems. Built upon the encoder‑decoder backbone of D‑LinkNet, D3FNet introduces three key innovations:(1) a Differential Attention Dilation Extraction (DADE) module that enhances subtle road features while suppressing background noise at the bottleneck; (2) a Dual‑stream Decoding Fusion Mechanism (DDFM) that integrates original and attention‑modulated features to balance spatial precision with semantic context; and (3) a multi‑scale dilation strategy (rates 1, 3, 5, 9) that mitigates gridding artifacts and improves continuity in narrow road prediction. Unlike conventional models that overfit to generic road widths, D3FNet specifically targets fine‑grained, occluded, and low‑contrast road segments. Extensive experiments on the DeepGlobe and CHN6‑CUG benchmarks show that D3FNet achieves superior IoU and recall on challenging road regions, outperforming state‑of‑the‑art baselines. Ablation studies further verify the complementary synergy of attention‑guided encoding and dual‑path decoding. These results confirm D3FNet as a robust solution for fine‑grained narrow road extraction in complex remote and cooperative perception scenarios.

Abstract:
Marine oil spills pose grave environmental and economic risks, threatening marine ecosystems, coastlines, and dependent industries. Predicting and managing oil spill trajectories is highly complex, due to the interplay of physical, chemical, and environmental factors such as wind, currents, and temperature, which makes timely and effective response challenging. Accurate real‑time trajectory forecasting and coordinated mitigation are vital for minimizing the impact of these disasters. This study introduces an integrated framework combining a multi‑agent swarm robotics system built on the MOOS‑IvP platform with Liquid Time‑Constant Neural Networks (LTCNs). The proposed system fuses adaptive machine learning with autonomous marine robotics, enabling real‑time prediction, dynamic tracking, and rapid response to evolving oil spills. By leveraging LTCNs‑‑well‑suited for modeling complex, time‑dependent processes‑‑the framework achieves real‑time, high‑accuracy forecasts of spill movement. Swarm intelligence enables decentralized, scalable, and resilient decision‑making among robot agents, enhancing collective monitoring and containment efforts. Our approach was validated using data from the Deepwater Horizon spill, where the LTC‑RK4 model achieved 0.96 spatial accuracy, surpassing LSTM approaches by 23%. The integration of advanced neural modeling with autonomous, coordinated robotics demonstrates substantial improvements in prediction precision, flexibility, and operational scalability. Ultimately, this research advances the state‑of‑the‑art for sustainable, autonomous oil spill management and environmental protection by enhancing both trajectory prediction and response coordination.

Abstract:
Multi‑vehicle trajectory planning (MVTP) is one of the key challenges in multi‑robot systems (MRSs) and has broad applications across various fields. This paper presents ESCoT, an enhanced step‑based coordinate trajectory planning method for multiple car‑like robots. ESCoT incorporates two key strategies: collaborative planning for local robot groups and replanning for duplicate configurations. These strategies effectively enhance the performance of step‑based MVTP methods. Through extensive experiments, we show that ESCoT 1) in sparse scenarios, significantly improves solution quality compared to baseline step‑based method, achieving up to 70% improvement in typical conflict scenarios and 34% in randomly generated scenarios, while maintaining high solving efficiency; and 2) in dense scenarios, outperforms all baseline methods, maintains a success rate of over 50% even in the most challenging configurations. The results demonstrate that ESCoT effectively solves MVTP, further extending the capabilities of step‑based methods. Finally, practical robot tests validate the algorithm's applicability in real‑world scenarios.

Abstract:
Collective behaviors such as swarming and flocking emerge from simple, decentralized interactions in biological systems. Existing models, such as Vicsek and Cucker‑Smale, lack collision avoidance, whereas the Olfati‑Saber model imposes rigid formations, limiting their applicability in swarm robotics. To address these limitations, this paper proposes a minimal yet expressive model that governs agent dynamics using relative positions, velocities, and local density, modulated by two tunable parameters: the spatial offset and kinetic offset. The model achieves spatially flexible, collision‑free behaviors that reflect naturalistic group dynamics. Furthermore, we extend the framework to cognitive autonomous systems, enabling energy‑aware phase transitions between swarming and flocking through adaptive control parameter tuning. This cognitively inspired approach offers a robust foundation for real‑world applications in multi‑robot systems, particularly autonomous aerial swarms.

Abstract:
The Iterative Forecast Planner (IFP) is a geometric planning approach that offers lightweight computations, scalable, and reactive solutions for multi‑robot path planning in decentralized, communication‑free settings. However, it struggles in symmetric configurations, where mirrored interactions often lead to collisions and deadlocks. We introduce eIFP‑MPC, an optimized and extended version of IFP that improves robustness and path consistency in dense, dynamic environments. The method refines threat prioritization using a time‑to‑collision heuristic, stabilizes path generation through cost‑based via‑point selection, and ensures dynamic feasibility by incorporating model predictive control (MPC) into the planning process. These enhancements are tightly integrated into the IFP to preserve its efficiency while improving its adaptability and stability. Extensive simulations across symmetric and high‑density scenarios show that eIFP‑MPC significantly reduces oscillations, ensures collision‑free motion, and improves trajectory efficiency. The results demonstrate that geometric planners can be strengthened through optimization, enabling robust performance at scale in complex multi‑agent environments.

Abstract:
As multi‑agent systems (MAS) become increasingly prevalent in autonomous systems, distributed control, and edge intelligence, efficient communication under resource constraints has emerged as a critical challenge. Traditional communication paradigms often emphasize message fidelity or bandwidth optimization, overlooking the task relevance of the exchanged information. In contrast, goal‑oriented communication prioritizes the importance of information with respect to the agents' shared objectives. This review provides a comprehensive survey of goal‑oriented communication in MAS, bridging perspectives from information theory, communication theory, and machine learning. We examine foundational concepts alongside learning‑based approaches and emergent protocols. Special attention is given to coordination under communication constraints, as well as applications in domains such as swarm robotics, federated learning, and edge computing. The paper concludes with a discussion of open challenges and future research directions at the intersection of communication theory, machine learning, and multi‑agent decision making.

Abstract:
The design of communication systems has traditionally prioritized the reliable and timely delivery of data. However, the scalability challenges faced by the evolution towards a data‑driven hyper‑connected society and economy demand new communication paradigms that carefully curate the content being transmitted. This paper proposes a joint semantic and task‑oriented communication paradigm where connected devices transmit only the information necessary to convey the desired meaning that is relevant to the intended receivers, based on their context. We qualitatively and quantitatively analyze the potential benefits of the proposed semantic and task‑oriented communication paradigm in the Vehicle‑to‑Everything (V2X) domain. The V2X domain offers a unique environment for the development and deployment of semantic and task‑oriented V2X communications, as it is rich in contextual information and Connected and Autonomous Vehicles (CAVs) are native semantic devices. The qualitative analysis focuses on a cooperative perception use case and shows how semantic and task‑oriented V2X communications can reduce the amount of information transmitted by each vehicle without compromising the situational awareness of its intended receivers. The quantitative analysis numerically demonstrates that semantic and task‑oriented V2X communications can achieve a two‑fold improvement in communication efficiency which can significantly benefit the scalability of future V2X networks.

Abstract:
Multi‑robot coordination is crucial for autonomous systems, yet real‑world deployments often encounter various failures. These include both temporary and permanent disruptions in sensing and communication, which can significantly degrade system robustness and performance if not explicitly modeled. Despite its practical importance, failure‑aware coordination remains underexplored in the literature. To bridge the gap between idealized conditions and the complexities of real‑world environments, we propose a unified failure‑aware coordination framework designed to enable resilient and adaptive multi‑robot target tracking under both temporary and permanent failure conditions. Our approach systematically distinguishes between two classes of failures: (1) probabilistic and temporary disruptions, where robots recover from intermittent sensing or communication losses by dynamically adapting paths and avoiding inferred danger zones, and (2) permanent failures, where robots lose sensing or communication capabilities irreversibly, requiring sustained, decentralized behavioral adaptation. To handle these scenarios, the robot team is partitioned into subgroups. Robots that remain connected form a communication group and collaboratively plan using partially centralized nonlinear optimization. Robots experiencing permanent disconnection or failure continue to operate independently through decentralized or individual optimization, allowing them to contribute to the task within their local context. We extensively evaluate our method across a range of benchmark variations and conduct a comprehensive assessment under diverse real‑world failure scenarios. Results show that our framework consistently achieves robust performance in realistic environments with unknown danger zones, offering a practical and generalizable solution for the multi‑robot systems community.

Abstract:
This paper introduces CoralGuide, a novel framework designed for path planning and trajectory optimization for tethered multi‑robot systems. We focus on marine robotics, which commonly have tethered configurations of an Autonomous Surface Vehicle (ASV) and an Autonomous Underwater Vehicle (AUV). CoralGuide provides safe navigation in marine environments by enhancing the A algorithm with specialized heuristics tailored for tethered ASV‑AUV systems. Our method integrates catenary curve modelling for tether management and employs Bezier curve interpolation for smoother trajectory planning, ensuring efficient and synchronized operations without compromising safety. Through simulations and real‑world experiments, we have validated CoralGuides effectiveness in improving path planning and trajectory optimization, demonstrating its potential to significantly enhance operational capabilities in marine research and infrastructure inspection.

Abstract:
This paper introduces a novel simulation framework for evaluating motion control in tethered multi‑robot systems within dynamic marine environments. Specifically, it focuses on the coordinated operation of an Autonomous Underwater Vehicle (AUV) and an Autonomous Surface Vehicle(ASV). The framework leverages GazeboSim, enhanced with realistic marine environment plugins and ArduPilots SoftwareIn‑The‑Loop (SITL) mode, to provide a high‑fidelity simulation platform. A detailed tether model, combining catenary equations and physical simulation, is integrated to accurately represent the dynamic interactions between the vehicles and the environment. This setup facilitates the development and testing of advanced control strategies under realistic conditions, demonstrating the frameworks capability to analyze complex tether interactions and their impact on system performance.

Abstract:
Gestures are an expressive input modality for controlling multiple robots, but their use is often limited by rigid mappings and recognition constraints. To move beyond these limitations, we propose roleplaying metaphors as a scaffold for designing richer interactions. By introducing three roles: Director, Puppeteer, and Wizard, we demonstrate how narrative framing can guide the creation of diverse gesture sets and interaction styles. These roles enable a variety of scenarios, showing how roleplay can unlock new possibilities for multi‑robot systems. Our approach emphasizes creativity, expressiveness, and intuitiveness as key elements for future human‑robot interaction design.

Abstract:
Cooperative perception (CP) enhances situational awareness of connected and autonomous vehicles by exchanging and combining messages from multiple agents. While prior work has explored adversarial integrity attacks that degrade perceptual accuracy, little is known about CP's robustness against attacks on timeliness (or availability), a safety‑critical requirement for autonomous driving. In this paper, we present CP‑FREEZER, the first latency attack that maximizes the computation delay of CP algorithms by injecting adversarial perturbation via V2V messages. Our attack resolves several unique challenges, including the non‑differentiability of point cloud preprocessing, asynchronous knowledge of the victim's input due to transmission delays, and uses a novel loss function that effectively maximizes the execution time of the CP pipeline. Extensive experiments show that CP‑FREEZER increases end‑to‑end CP latency by over 90×, pushing per‑frame processing time beyond 3 seconds with a 100% success rate on our real‑world vehicle testbed. Our findings reveal a critical threat to the availability of CP systems, highlighting the urgent need for robust defenses.

Abstract:
The proposal introduces an innovative drone swarm perception system that aims to solve problems related to computational limitations and low‑bandwidth communication, and real‑time scene reconstruction. The framework enables efficient multi‑agent 3D/4D scene synthesis through federated learning of shared diffusion model and YOLOv12 lightweight semantic extraction and local NeRF updates while maintaining privacy and scalability. The framework redesigns generative diffusion models for joint scene reconstruction, and improves cooperative scene understanding, while adding semantic‑aware compression protocols. The approach can be validated through simulations and potential real‑world deployment on drone testbeds, positioning it as a disruptive advancement in multi‑agent AI for autonomous systems.

Abstract:
Connected autonomous vehicles (CAVs) must simultaneously perform multiple tasks, such as perception, prediction, planning, and control, to ensure safe and reliable navigation in complex environments. Moreover, through vehicle‑to‑everything (V2X) communication, cooperative perception and driving among CAVs can be enabled, thereby mitigating the limitations of individual vehicles, while it also introduces stringent latency, reliability, and bandwidth constraints. Traditionally, tasks are addressed using separate models, which leads to high deployment costs, increased computational overhead, and challenges in achieving real‑time performance. Multi‑task learning (MTL) has recently emerged as a promising solution that enables the joint learning of multiple tasks within a unified model. This offers improved efficiency and resource utilization. To the best of our knowledge, this survey is the first comprehensive review focusing on deep MTL in CAVs. We begin with an overview of CAVs and MTL to provide foundational background. Then, we review MTL approaches across key functional domains in CAVs, including perception, prediction, planning, control, as well as V2X communications and radio resource management (RRM). For the first four domains, we categorize existing works under ego vehicle‑only (onboard‑only) and V2X‑enhanced cooperative (multi‑agent) paradigms. We further discuss V2X communications and RRM as communication‑centric MTL problems. Finally, we discuss the strengths and limitations of existing methods, identify key research gaps, and provide future research directions aimed at advancing MTL methodologies for CAV systems.

Abstract:
Collective decision‑making is a key function of autonomous robot swarms, enabling them to reach a consensus on actions based on environmental features. Existing strategies require the participation of all robots in the decision‑making process, which is resource‑intensive and prevents the swarm from allocating the robots to any other tasks. We propose Subset‑Based Collective Decision‑Making (SubCDM), which enables decisions using only a swarm subset. The construction of the subset is dynamic and decentralized, relying solely on local information. Our method allows the swarm to adaptively determine the size of the subset for accurate decision‑making, depending on the difficulty of reaching a consensus. Simulation results using one hundred robots show that our approach achieves accuracy comparable to using the entire swarm while reducing the number of robots required to perform collective decision‑making, making it a resource‑efficient solution for collective decision‑making in swarm robotics.

Abstract:
Multi‑robot SLAM aims at localizing and building a map with multiple robots, interacting with each other. In the work described in this article, we analyze the pipeline of a decentralized LiDAR SLAM system to study the current limitations of the state of the art, and we discover a significant source of failures, i.e., that the loop detection is the source of too many false positives. We therefore develop and propose a new heuristic to overcome these limitations. The environment taken as reference in this work is the highly challenging case of underground tunnels. We also highlight potential new research areas still under‑explored.

Abstract:
Multi‑robot coordination has traditionally relied on a mission‑specific and expert‑driven pipeline, where natural language mission descriptions are manually translated by domain experts into mathematical formulation, algorithm design, and executable code. This conventional process is labor‑intensive, inaccessible to non‑experts, and inflexible to changes in mission requirements. Here, we propose LAN2CB (Language to Collective Behavior), a novel framework that leverages large language models (LLMs) to streamline and generalize the multi‑robot coordination pipeline. LAN2CB transforms natural language (NL) mission descriptions into executable Python code for multi‑robot systems through two core modules: (1) Mission Analysis, which parses mission descriptions into behavior trees, and (2) Code Generation, which leverages the behavior tree and a structured knowledge base to generate robot control code. We further introduce a dataset of natural language mission descriptions to support development and benchmarking. Experiments in both simulation and real‑world environments demonstrate that LAN2CB enables robust and flexible multi‑robot coordination from natural language, significantly reducing manual engineering effort and supporting broad generalization across diverse mission types. Website: https://sites.google.com/view/lan‑cb

Abstract:
The design of control systems for the spatial self‑organization of mobile agents is an open challenge across several engineering domains, including swarm robotics and synthetic biology. Here, we propose a bio‑inspired leader‑follower solution, which is aware of energy constraints of mobile agents and is apt to deal with large swarms. Akin to many natural systems, control objectives are formulated for the entire collective, and leaders and followers are allowed to plastically switch their role in time. We frame a density control problem, modeling the agents' population via a system of nonlinear partial differential equations. This approach allows for a compact description that inherently avoids the curse of dimensionality and improves analytical tractability. We derive analytical guarantees for the existence of desired steady‑state solutions and their local stability for one‑dimensional and higher‑dimensional problems. We numerically validate our control methodology, offering support to the effectiveness, robustness, and versatility of our proposed bio‑inspired control strategy.

Abstract:
The deployment of simple emergent behaviors in swarm robotics has been well‑rehearsed in the literature. A recent study has shown how self‑aggregation is possible in a multitask approach ‑‑ where multiple self‑aggregation task instances occur concurrently in the same environment. The multitask approach poses new challenges, in special, how the dynamic of each group impacts the performance of others. So far, the multitask self‑aggregation of groups of robots suffers from generating a circular formation ‑‑ that is not fully compact ‑‑ or is not fully autonomous. In this paper, we present a multitask self‑aggregation where groups of homogeneous robots sort themselves into different compact clusters, relying solely on a line‑of‑sight sensor. Our multitask self‑aggregation behavior was able to scale well and achieve a compact formation. We report scalability results from a series of simulation trials with different configurations in the number of groups and the number of robots per group. We were able to improve the multitask self‑aggregation behavior performance in terms of the compactness of the clusters, keeping the proportion of clustered robots found in other studies.

Abstract:
Multi‑robot localization is a crucial task for implementing multi‑robot systems. Numerous researchers have proposed optimization‑based multi‑robot localization methods that use camera, IMU, and UWB sensors. Nevertheless, characteristics of individual robot odometry estimates and distance measurements between robots used in the optimization are not sufficiently considered. In addition, previous researches were heavily influenced by the odometry accuracy that is estimated from individual robots. Consequently, long‑term drift error caused by error accumulation is potentially inevitable. In this paper, we propose a novel visual‑inertial‑range‑based multi‑robot localization method, named SaWa‑ML, which enables geometric structure‑aware pose correction and weight adaptation‑based robust multi‑robot localization. Our contributions are twofold: (i) we leverage UWB sensor data, whose range error does not accumulate over time, to first estimate the relative positions between robots and then correct the positions of each robot, thus reducing long‑term drift errors, (ii) we design adaptive weights for robot pose correction by considering the characteristics of the sensor data and visual‑inertial odometry estimates. The proposed method has been validated in real‑world experiments, showing a substantial performance increase compared with state‑of‑the‑art algorithms.

Abstract:
Real‑world Vehicle‑to‑Everything (V2X) cooperative perception systems often operate under heterogeneous sensor configurations due to cost constraints and deployment variability across vehicles and infrastructure. This heterogeneity poses significant challenges for feature fusion and perception reliability. To address these issues, we propose HeCoFuse, a unified framework designed for cooperative perception across mixed sensor setups where nodes may carry Cameras (C), LiDARs (L), or both. By introducing a hierarchical fusion mechanism that adaptively weights features through a combination of channel‑wise and spatial attention, HeCoFuse can tackle critical challenges such as cross‑modality feature misalignment and imbalanced representation quality. In addition, an adaptive spatial resolution adjustment module is employed to balance computational cost and fusion effectiveness. To enhance robustness across different configurations, we further implement a cooperative learning strategy that dynamically adjusts fusion type based on available modalities. Experiments on the real‑world TUMTraf‑V2X dataset demonstrate that HeCoFuse achieves 43.22% 3D mAP under the full sensor configuration (LC+LC), outperforming the CoopDet3D baseline by 1.17%, and reaches an even higher 43.38% 3D mAP in the L+LC scenario, while maintaining 3D mAP in the range of 21.74% to 43.38% across nine heterogeneous sensor configurations. These results, validated by our first‑place finish in the CVPR 2025 DriveX challenge, establish HeCoFuse as the current state‑of‑the‑art on TUM‑Traf V2X dataset while demonstrating robust performance across diverse sensor deployments.

Abstract:
In this paper, we introduce Hebbian learning as a novel method for swarm robotics, enabling the automatic emergence of heterogeneity. Hebbian learning presents a biologically inspired form of neural adaptation that solely relies on local information. By doing so, we resolve several major challenges for learning heterogeneous control: 1) Hebbian learning removes the complexity of attributing emergent phenomena to single agents through local learning rules, thus circumventing the micro‑macro problem; 2) uniform Hebbian learning rules across all swarm members limit the number of parameters needed, mitigating the curse of dimensionality with scaling swarm sizes; and 3) evolving Hebbian learning rules based on swarm‑level behaviour minimises the need for extensive prior knowledge typically required for optimising heterogeneous swarms. This work demonstrates that with Hebbian learning heterogeneity naturally emerges, resulting in swarm‑level behavioural switching and in significantly improved swarm capabilities. It also demonstrates how the evolution of Hebbian learning rules can be a valid alternative to Multi Agent Reinforcement Learning in standard benchmarking tasks.

Abstract:
We propose a multi‑robot control paradigm to solve point‑to‑point navigation tasks for a team of holonomic robots with access to the full environment information. The framework invokes two processes asynchronously at high frequency: (i) a centralized, discrete, and full‑horizon planner for computing collision‑ and deadlock‑free paths rapidly, leveraging recent advances in multi‑agent pathfinding (MAPF), and (ii) dynamics‑aware, robot‑wise optimal trajectory controllers that ensure all robots independently follow their assigned paths reliably. This hierarchical shift in planning representation from (i) discrete and coupled to (ii) continuous and decoupled domains enables the framework to maintain long‑term scalable motion synthesis. As an instantiation of this idea, we present LF, which combines a fast state‑of‑the‑art MAPF solver (LaCAM), and a robust feedback control stack (Freyja) for executing agile robot maneuvers. LF provides a robust and versatile mechanism for lifelong multi‑robot navigation even under asynchronous and partial goal updates, and adapts to dynamic workspaces simply by quick replanning. We present various multirotor and ground robot demonstrations, including the deployment of 15 real multirotors with random, consecutive target updates while a person walks through the operational workspace.

Abstract:
Autonomous trucking offers significant benefits, such as improved safety and reduced costs, but faces unique perception challenges due to trucks' large size and dynamic trailer movements. These challenges include extensive blind spots and occlusions that hinder the truck's perception and the capabilities of other road users. To address these limitations, cooperative perception emerges as a promising solution. However, existing datasets predominantly feature light vehicle interactions or lack multi‑agent configurations for heavy‑duty vehicle scenarios. To bridge this gap, we introduce TruckV2X, the first large‑scale truck‑centered cooperative perception dataset featuring multi‑modal sensing (LiDAR and cameras) and multi‑agent cooperation (tractors, trailers, CAVs, and RSUs). We further investigate how trucks influence collaborative perception needs, establishing performance benchmarks while suggesting research priorities for heavy vehicle perception. The dataset provides a foundation for developing cooperative perception systems with enhanced occlusion handling capabilities, and accelerates the deployment of multi‑agent autonomous trucking systems. The TruckV2X dataset is available at https://huggingface.co/datasets/XieTenghu1/TruckV2X.

Abstract:
Emergence and swarms are widely discussed topics, yet no consensus exists on their formal definitions. This lack of agreement makes it difficult not only for new researchers to grasp these concepts, but also for experts who may use the same terms to mean different things. Many attempts have been made to objectively define 'swarm' or 'emergence,' with recent work highlighting the role of the external observer. Still, several researchers argue that once an observer's vantage point (e.g., scope, resolution, context) is established, the terms can be made objective or measured quantitatively. In this note, we propose a framework to discuss these ideas rigorously by separating externally observable states from latent, unobservable ones. This allows us to compare and contrast existing definitions of swarms and emergence on common ground. We argue that these concepts are ultimately subjective‑shaped less by the system itself than by the perception and tacit knowledge of the observer. Specifically, we suggest that a 'swarm' is not defined by its group behavior alone, but by the process generating that behavior. Our broader goal is to support the design and deployment of robotic swarm systems, highlighting the critical distinction between multi‑robot systems and true swarms.

Abstract:
Efficient exploration is a well known problem in deep reinforcement learning and this problem is exacerbated in multi‑agent reinforcement learning due the intrinsic complexities of such algorithms. There are several approaches to efficiently explore an environment to learn to solve tasks by multi‑agent operating in that environment, of which, the idea of expert exploration is investigated in this work. More specifically, this work investigates the application of large‑language models as expert planners for efficient exploration in planning based tasks for multiple agents.

Abstract:
In multi‑robot systems (MRS), cooperative localization is a crucial task for enhancing system robustness and scalability, especially in GPS‑denied or communication‑limited environments. However, adversarial attacks, such as sensor manipulation, and communication jamming, pose significant challenges to the performance of traditional localization methods. In this paper, we propose a novel distributed fault‑tolerant cooperative localization framework to enhance resilience against sensor and communication disruptions in adversarial environments. We introduce an adaptive event‑triggered communication strategy that dynamically adjusts communication thresholds based on real‑time sensing and communication quality. This strategy ensures optimal performance even in the presence of sensor degradation or communication failure. Furthermore, we conduct a rigorous analysis of the convergence and stability properties of the proposed algorithm, demonstrating its resilience against bounded adversarial zones and maintaining accurate state estimation. Robotarium‑based experiment results show that our proposed algorithm significantly outperforms traditional methods in terms of localization accuracy and communication efficiency, particularly in adversarial settings. Our approach offers improved scalability, reliability, and fault tolerance for MRS, making it suitable for large‑scale deployments in real‑world, challenging environments.

Abstract:
Understanding leadership dynamics in collective behavior is a key challenge in animal ecology, swarm robotics, and intelligent transportation. Traditional information‑theoretic approaches, including Transfer Entropy (TE) and Time‑Lagged Mutual Information (TLMI), have been widely used to infer leader‑follower relationships but face critical limitations in noisy or short‑duration datasets due to their reliance on robust probability estimations. This study proposes a method based on dynamic network inference using time‑lagged correlations across multiple kinematic variables: velocity, acceleration, and direction. Our approach constructs directed influence graphs over time, enabling the identification of leadership patterns without the need for large volumes of data or parameter‑sensitive discretization. We validate our method through two multi‑agent simulations in NetLogo: a modified Vicsek model with informed leaders and a predator‑prey model featuring coordinated and independent wolf groups. Experimental results demonstrate that the network‑based method outperforms TE and TLMI in scenarios with limited spatiotemporal observations, ranking true leaders at the top of influence metrics more consistently than TE and TLMI.

Abstract:
Vehicle‑infrastructure (V2I) cooperative perception can substantially extend the range, coverage, and robustness of autonomous driving systems beyond the limits of onboard‑only sensing, particularly in occluded and adverse‑weather environments. However, its practical value is still difficult to quantify because existing benchmarks do not adequately capture large‑scale multi‑node deployments, realistic communication conditions, and adverse‑weather operation. This paper presents CoInfra, a deployable cooperative infrastructure perception platform comprising 14 roadside sensor nodes connected through a commercial 5G network, together with a large‑scale dataset and an open‑source system stack for V2I cooperation research. The system supports synchronized multi‑node sensing and delay‑aware fusion under real 5G communication constraints. The released dataset covers an eight‑node urban roundabout under four weather conditions (sunny, rainy, heavy snow, and freezing rain) and contains 294k LiDAR frames, 589k camera images, and 332k globally consistent 3D bounding boxes. It also includes a synchronized V2I subset collected with an autonomous vehicle. Beyond standard perception benchmarks, we further evaluate whether infrastructure sensing improves awareness of safety‑critical traffic participants during roundabout interactions. In structured conflict scenarios, V2I cooperation increases critical‑frame completeness from 33%‑46% with vehicle‑only sensing to 86%‑100%. These results show that multi‑node infrastructure perception can significantly improve situational awareness in conflict‑rich traffic scenarios where vehicle‑only sensing is most limited.

Abstract:
The problem of multi‑robot coverage control becomes significantly challenging when multiple robots leave the mission space simultaneously to charge their batteries, disrupting the underlying network topology for communication and sensing. To address this, we propose a resilient network design and control approach that allows robots to achieve the desired coverage performance while satisfying energy constraints and maintaining network connectivity throughout the mission. We model the combined motion, energy, and network dynamics of the multirobot systems (MRS) as a hybrid system with three modes, i.e., coverage, return‑to‑base, and recharge, respectively. We show that ensuring the energy constraints can be transformed into designing appropriate guard conditions for mode transition between each of the three modes. Additionally, we present a systematic procedure to design, maintain, and reconfigure the underlying network topology using an energy‑aware bearing rigid network design, enhancing the structural resilience of the MRS even when a subset of robots departs to charge their batteries. Finally, we validate our proposed method using numerical simulations.

Abstract:
This paper introduces a novel intention‑sharing mechanism for Electrically Power‑Assisted Cycles (EPACs) within V2X communication frameworks, enhancing the ETSI VRU Awareness Message (VAM) protocol. The method replaces discrete predicted trajectory points with a compact elliptical geographical area representation derived via quadratic polynomial fitting and Least Squares Method (LSM). This approach encodes trajectory predictions with fixed‑size data payloads, independent of the number of forecasted points, enabling higher‑frequency transmissions and improved network reliability. Simulation results demonstrate superior inter‑packet gap (IPG) performance compared to standard ETSI VAMs, particularly under constrained communication conditions. A physical experiment validates the feasibility of real‑time deployment on embedded systems. The method supports scalable, low‑latency intention sharing, contributing to cooperative perception and enhanced safety for vulnerable road users in connected and automated mobility ecosystems. Finally, we discuss the viability of LSM and open the door to other methods for prediction.

Abstract:
Operations in disaster response, search \& rescue, and military missions that involve multiple agents demand automated processes to support the planning of the courses of action (COA). Moreover, traverse‑affecting changes in the environment (rain, snow, blockades, etc.) may impact the expected performance of a COA, making it desirable to have a pool of COAs that are diverse in task distributions across agents. Further, variations in agent capabilities, which could be human crews and/or autonomous systems, present practical opportunities and computational challenges to the planning process. This paper presents a new theoretical formulation and computational framework to generate such diverse pools of COAs for operations with soft variations in agent‑task compatibility. Key to the problem formulation is a graph abstraction of the task space and the pool of COAs itself to quantify its diversity. Formulating the COAs as a centralized multi‑robot task allocation problem, a genetic algorithm is used for (order‑ignoring) allocations of tasks to each agent that jointly maximize diversity within the COA pool and overall compatibility of the agent‑task mappings. A graph neural network is trained using a policy gradient approach to then perform single agent task sequencing in each COA, which maximizes completion rates adaptive to task features. Our tests of the COA generation process in a simulated environment demonstrate significant performance gain over a random walk baseline, small optimality gap in task sequencing, and execution time of about 50 minutes to plan up to 20 COAs for 5 agent/100 task operations.

Abstract:
Multi‑sensor fusion perception (MSFP) is a key technology for embodied AI, which can serve a variety of downstream tasks (e.g., 3D object detection and semantic segmentation) and application scenarios (e.g., autonomous driving and swarm robotics). Recently, impressive achievements on AI‑based MSFP methods have been reviewed in relevant surveys. However, we observe that the existing surveys have some limitations after a rigorous and detailed investigation. For one thing, most surveys are oriented to a single task or research field, such as 3D object detection or autonomous driving. Therefore, researchers in other related tasks often find it difficult to benefit directly. For another, most surveys only introduce MSFP from a single perspective of multi‑modal fusion, while lacking consideration of the diversity of MSFP methods, such as multi‑view fusion and time‑series fusion. To this end, in this paper, we hope to organize MSFP research from a task‑agnostic perspective, where methods are reported from various technical views. Specifically, we first introduce the background of MSFP. Next, we review multi‑modal and multi‑agent fusion methods. A step further, time‑series fusion methods are analyzed. In the era of LLM, we also investigate multimodal LLM fusion methods. Finally, we discuss open challenges and future directions for MSFP. We hope this survey can help researchers understand the important progress in MSFP and provide possible insights for future research.

Abstract:
Multi‑robot systems are emerging as a promising solution to the growing demand for productivity, safety, and adaptability across industrial sectors. However, effectively coordinating multiple robots in dynamic and uncertain environments, such as construction sites, remains a challenge, particularly due to unpredictable factors like material delays, unexpected site conditions, and weather‑induced disruptions. To address these challenges, this study proposes an adaptive task allocation framework that strategically leverages the synergistic potential of Digital Twins, Integer Programming (IP), and Large Language Models (LLMs). The multi‑robot task allocation problem is formally defined and solved using an IP model that accounts for task dependencies, robot heterogeneity, scheduling constraints, and re‑planning requirements. A mechanism for narrative‑driven schedule adaptation is introduced, in which unstructured natural language inputs are interpreted by an LLM, and optimization constraints are autonomously updated, enabling human‑in‑the‑loop flexibility without manual coding. A digital twin‑based system has been developed to enable real‑time synchronization between physical operations and their digital representations. This closed‑loop feedback framework ensures that the system remains dynamic and responsive to ongoing changes on site. A case study demonstrates both the computational efficiency of the optimization algorithm and the reasoning performance of several LLMs, with top‑performing models achieving over 97% accuracy in constraint and parameter extraction. The results confirm the practicality, adaptability, and cross‑domain applicability of the proposed methods.

Abstract:
Zero‑shot coordination (ZSC) ‑‑ the ability to collaborate with unfamiliar partners ‑‑ is essential to making autonomous agents effective teammates. Existing ZSC methods evaluate coordination capabilities between two agents who have not previously interacted. However, these scenarios do not reflect the complexity of real‑world multi‑agent systems, where coordination often involves a hierarchy of sub‑groups and interactions between teams of agents, known as Multi‑Team Systems (MTS). To address this gap, we first introduce N‑player Overcooked, an N‑agent extension of the popular two‑agent ZSC benchmark, enabling evaluation of ZSC in N‑agent scenarios. We then propose N‑XPlay for ZSC in N‑agent, multi‑team settings. Comparison against Self‑Play across two‑, three‑ and five‑player Overcooked scenarios, where agents are split between an ``ego‑team'' and a group of unseen collaborators shows that agents trained with N‑XPlay are better able to simultaneously balance ``intra‑team'' and ``inter‑team'' coordination than agents trained with SP.

Abstract:
Signal source localization has been a problem of interest in the multi‑robot systems domain given its applications in search & rescue and hazard localization in various industrial and outdoor settings. A variety of multi‑robot search algorithms exist that usually formulate and solve the associated autonomous motion planning problem as a heuristic model‑free or belief model‑based optimization process. Most of these algorithms however remains tested only in simulation, thereby losing the opportunity to generate knowledge about how such algorithms would compare/contrast in a real physical setting in terms of search performance and real‑time computing performance. To address this gap, this paper presents a new lab‑scale physical setup and associated open‑source software pipeline to evaluate and benchmark multi‑robot search algorithms. The presented physical setup innovatively uses an acoustic source (that is safe and inexpensive) and small ground robots (e‑pucks) operating in a standard motion‑capture environment. This setup can be easily recreated and used by most robotics researchers. The acoustic source also presents interesting uncertainty in terms of its noise‑to‑signal ratio, which is useful to assess sim‑to‑real gaps. The overall software pipeline is designed to readily interface with any multi‑robot search algorithm with minimal effort and is executable in parallel asynchronous form. This pipeline includes a framework for distributed implementation of multi‑robot or swarm search algorithms, integrated with a ROS (Robotics Operating System)‑based software stack for motion capture supported localization. The utility of this novel setup is demonstrated by using it to evaluate two state‑of‑the‑art multi‑robot search algorithms, based on swarm optimization and batch‑Bayesian Optimization (called Bayes‑Swarm), as well as a random walk baseline.

Abstract:
Risk quantification is a critical component of safe autonomous driving, however, constrained by the limited perception range and occlusion of single‑vehicle systems in complex and dense scenarios. Vehicle‑to‑everything (V2X) paradigm has been a promising solution to sharing complementary perception information, nevertheless, how to ensure the risk interpretability while understanding multi‑agent interaction with V2X remains an open question. In this paper, we introduce the first V2X‑enabled risk quantification pipeline, CooperRisk, to fuse perception information from multiple agents and quantify the scenario driving risk in future multiple timestamps. The risk is represented as a scenario risk map to ensure interpretability based on risk severity and exposure, and the multi‑agent interaction is captured by the learning‑based cooperative prediction model. We carefully design a risk‑oriented transformer‑based prediction model with multi‑modality and multi‑agent considerations. It aims to ensure scene‑consistent future behaviors of multiple agents and avoid conflicting predictions that could lead to overly conservative risk quantification and cause the ego vehicle to become overly hesitant to drive. Then, the temporal risk maps could serve to guide a model predictive control planner. We evaluate the CooperRisk pipeline in a real‑world V2X dataset V2XPnP, and the experiments demonstrate its superior performance in risk quantification, showing a 44.35% decrease in conflict rate between the ego vehicle and background traffic participants.

Abstract:
The self‑organization of robots for the formation of structures and shapes is a stimulating application of the swarm robotic system. It involves a large number of autonomous robots of heterogeneous behavior, coordination among them, and their interaction with the dynamic environment. This process of complex structure formation is considered a complex system, which needs to be modeled by using any modeling approach. Although the formal specification approach along with other formal methods has been used to model the behavior of robots in a swarm. However, to the best of our knowledge, the formal specification approach has not been used to model the self‑organization process in swarm robotic systems for shape formation. In this paper, we use a formal specification approach to model the shape formation task of swarm robots. We use Z (Zed) language of formal specification, which is a state‑based language, to model the states of the entities of the systems. We demonstrate the effectiveness of Z for the self‑organized shape formation. The presented formal specification model gives the outlines for designing and implementing the swarm robotic system for the formation of complex shapes and structures. It also provides the foundation for modeling the complex shape formation process for swarm robotics using a multi‑agent system in a simulation‑based environment. Keywords: Swarm robotics, Self‑organization, Formal specification, Complex systems

Abstract:
Open multi‑agent systems are increasingly important in modeling real‑world applications, such as smart grids, swarm robotics, etc. In this paper, we aim to investigate a recently proposed problem for open multi‑agent systems, referred to as n‑agent ad hoc teamwork (NAHT), where only a number of agents are controlled. Existing methods tend to be based on heuristic design and consequently lack theoretical rigor and ambiguous credit assignment among agents. To address these limitations, we model and solve NAHT through the lens of cooperative game theory. More specifically, we first model an open multi‑agent system, characterized by its value, as an instance situated in a space of cooperative games, generated by a set of basis games. We then extend this space, along with the state space, to accommodate dynamic scenarios, thereby characterizing NAHT. Exploiting the justifiable assumption that basis game values correspond to a sequence of n‑step returns with different horizons, we represent the state values for NAHT in a form similar to λ‑returns. Furthermore, we derive Shapley values to allocate state values to the controlled agents, as credits for their contributions to the ad hoc team. Different from the conventional approach to shaping Shapley values in an explicit form, we shape Shapley values by fulfilling the three axioms uniquely describing them, well defined on the extended game space describing NAHT. To estimate Shapley values in dynamic scenarios, we propose a TD(λ)‑like algorithm. The resulting reinforcement learning (RL) algorithm is referred to as Shapley Machine. To our best knowledge, this is the first time that the concepts from cooperative game theory are directly related to RL concepts. In experiments, we demonstrate the effectiveness of Shapley Machine and verify reasonableness of our theory.

Abstract:
The labeled MRPP (Multi‑Robot Path Planning) problem involves routing robots from start to goal configurations efficiently while avoiding collisions. Despite progress in solution quality and runtime, its complexity and industrial relevance continue to drive research. This dissertation introduces scalable MRPP methods with provable guarantees and practical heuristics. First, we study dense MRPP on 2D grids, relevant to warehouse and parcel systems. We propose the Rubik Table method, achieving (1 + δ)‑optimal makespan (with δ\in (0, 0.5]) for up to \fracm_1 m_22 robots, solving large instances efficiently and setting a new theoretical benchmark. Next, we address real‑world MRPP. We design optimal layouts for structured environments (e.g., warehouses, parking systems) and propose a puzzle‑based system for dense, deadlock‑free autonomous vehicle parking. We also extend MRPP to Reeds‑Shepp robots, introducing motion primitives and smoothing techniques to ensure feasible, efficient paths under nonholonomic constraints. Simulations and real‑world tests validate the approach in urban driving and robotic transport scenarios.

Abstract:
Wireless communication‑based multi‑robot systems open the door to cyberattacks that can disrupt safety and performance of collaborative robots. The physical channel supporting inter‑robot communication offers an attractive opportunity to decouple the detection of malicious robots from task‑relevant data exchange between legitimate robots. Yet, trustworthiness indications coming from physical channels are uncertain and must be handled with this in mind. In this paper, we propose a resilient protocol for multi‑robot operation wherein a parameter λt accounts for how confident a robot is about the legitimacy of nearby robots that the physical channel indicates. Analytical results prove that our protocol achieves resilient coordination with arbitrarily many malicious robots under mild assumptions. Tuning λt allows a designer to trade between near‑optimal inter‑robot coordination and quick task execution; see Fig. 1. This is a fundamental performance tradeoff and must be carefully evaluated based on the task at hand. The effectiveness of our approach is numerically verified with experiments involving platoons of autonomous cars where some vehicles are maliciously spoofed.

Abstract:
Accurately perceiving complex driving environments is essential for ensuring the safe operation of autonomous vehicles. With the tremendous progress in deep learning and communication technologies, cooperative perception with Vehicle‑to‑Everything (V2X) technologies has emerged as a solution to overcome the limitations of single‑agent perception systems in perceiving distant objects and occlusions. Despite the considerable advancements, V2X cooperative perception systems require thorough testing and continuous enhancement of system performance. Given that V2X driving scenes entail intricate communications with multiple vehicles across various geographic locations, creating V2X test scenes for these systems poses a significant challenge. Moreover, current testing methodologies rely on manual data collection and labeling, which are both time‑consuming and costly. In this paper, we design and implement V2XGen, an automated testing generation tool for V2X cooperative perception systems. V2XGen utilizes a high‑fidelity approach to generate realistic cooperative object instances and strategically place them within the background data in crucial positions. Furthermore, V2XGen adopts a fitness‑guided V2X scene generation strategy for the transformed scene generation process and improves testing efficiency. We conduct experiments on V2XGen using multiple cooperative perception systems with different fusion schemes to assess its performance on various tasks. The experimental results demonstrate that V2XGen is capable of generating realistic test scenes and effectively detecting erroneous behaviors in different V2X‑oriented driving conditions. Furthermore, the results validate that retraining systems under test with the generated scenes can enhance average detection precision while reducing occlusion and long‑range perception errors.

Abstract:
We consider the Multi‑Robot Task Allocation (MRTA) problem that aims to optimize an assignment of multiple robots to multiple tasks in challenging environments which are with densely populated obstacles and narrow passages. In such environments, conventional methods optimizing the sum‑of‑cost are often ineffective because the conflicts between robots incur additional costs (e.g., collision avoidance, waiting). Also, an allocation that does not incorporate the actual robot paths could cause deadlocks, which significantly degrade the collective performance of the robots. We propose a scalable MRTA method that considers the paths of the robots to avoid collisions and deadlocks which result in a fast completion of all tasks (i.e., minimizing the makespan). To incorporate robot paths into task allocation, the proposed method constructs a roadmap using a Generalized Voronoi Diagram. The method partitions the roadmap into several components to know how to redistribute robots to achieve all tasks with less conflicts between the robots. In the redistribution process, robots are transferred to their final destinations according to a push‑pop mechanism with the first‑in first‑out principle. From the extensive experiments, we show that our method can handle instances with hundreds of robots in dense clutter while competitors are unable to compute a solution within a time limit.

Abstract:
Deploying multi‑robot systems in underwater environments is expensive and lengthy; testing algorithms and software in simulation improves development by decoupling software and hardware. However, this requires a simulation framework that closely resembles the real‑world. Angler is an open‑source framework that simulates low‑level communication protocols for an onboard autopilot, such as ArduSub, providing a framework that is close to reality, but unfortunately lacking support for simulating multiple robots. We present an extension to Angler that supports multi‑robot simulation and motion planning. Our extension has a modular architecture that creates non‑conflicting communication channels between Gazebo, ArduSub Software‑in‑the‑Loop (SITL), and MAVROS to operate multiple robots simultaneously in the same environment. Our multi‑robot motion planning module interfaces with cascaded controllers via a JointTrajectory controller in ROS~2. We also provide an integration with the Open Motion Planning Library (OMPL), a collision avoidance module, and tools for procedural environment generation. Our work enables the development and benchmarking of underwater multi‑robot motion planning in dynamic environments.

Abstract:
Cooperative autonomous robotic systems have significant potential for executing complex multi‑task missions across space, air, ground, and maritime domains. But they commonly operate in remote, dynamic and hazardous environments, requiring rapid in‑mission adaptation without reliance on fragile or slow communication links to centralised compute. Fast, on‑board replanning algorithms are therefore needed to enhance resilience. Reinforcement Learning shows strong promise for efficiently solving mission planning tasks when formulated as Travelling Salesperson Problems (TSPs), but existing methods: 1) are unsuitable for replanning, where agents do not start at a single location; 2) do not allow cooperation between agents; 3) are unable to model tasks with variable durations; or 4) lack practical considerations for on‑board deployment. Here we define the Cooperative Mission Replanning Problem as a novel variant of multiple TSP with adaptations to overcome these issues, and develop a new encoder/decoder‑based model using Graph Attention Networks and Attention Models to solve it effectively and efficiently. Using a simple example of cooperative drones, we show our replanner consistently (90% of the time) maintains performance within 10% of the state‑of‑the‑art LKH3 heuristic solver, whilst running 85‑370 times faster on a Raspberry Pi. This work paves the way for increased resilience in autonomous multi‑agent systems.

Abstract:
Cooperative perception enables vehicles to share sensor readings and has become a new paradigm to improve driving safety, where the key enabling technology for realizing this vision is to real‑time and accurately align and fuse the perceptions. Recent advances to align the views rely on high‑density LiDAR data or fine‑grained image feature representations, which however fail to meet the requirements of accuracy, real‑time, and adaptability for autonomous driving. To this end, we present MMatch, a lightweight system that enables accurate and real‑time perception fusion with mmWave radar point clouds. The key insight is that fine‑grained spatial information provided by the radar present unique associations with all the vehicles even in two separate views. As a result, by capturing and understanding the unique local and global position of the targets in this association, we can quickly find out all the co‑visible vehicles for view alignment. We implement MMatch on both the datasets collected from the CARLA platform and the real‑world traffic with over 15,000 radar point cloud pairs. Experimental results show that MMatch achieves decimeter‑level accuracy within 59ms, which significantly improves the reliability for autonomous driving.

Abstract:
As robots are increasingly deployed to collaborate on tasks within shared workspaces and resources, the failure of an individual robot can critically affect the group's performance. This issue is particularly challenging when robots lack global information or direct communication, relying instead on social interaction for coordination and to complete their tasks. In this study, we propose a novel fault‑tolerance technique leveraging physical contact interactions in multi‑robot systems, specifically under conditions of limited sensing and spatial confinement. We introduce the "Active Contact Response" (ACR) method, where each robot modulates its behavior based on the likelihood of encountering an inoperative (faulty) robot. Active robots are capable of collectively repositioning stationary and faulty peers to reduce obstructions and maintain optimal group functionality. We implement our algorithm in a team of autonomous robots, equipped with contact‑sensing and collision‑tolerance capabilities, tasked with collectively excavating cohesive model pellets. Experimental results indicate that the ACR method significantly improves the system's recovery time from robot failures, enabling continued collective excavation with minimal performance degradation. Thus, this work demonstrates the potential of leveraging local, social, and physical interactions to enhance fault tolerance and coordination in multi‑robot systems operating in constrained and extreme environments.

Abstract:
Autonomous robots are increasingly deployed for long‑term information‑gathering tasks, which pose two key challenges: planning informative trajectories in environments that evolve across space and time, and ensuring persistent operation under energy constraints. This paper presents a unified framework, mEclares, that addresses both challenges through adaptive ergodic search and energy‑aware scheduling in multi‑robot systems. Our contributions are two‑fold: (1) we model real‑world variability using stochastic spatiotemporal environments, where the underlying information evolves unpredictably due to process uncertainty. To guide exploration, we construct a target information spatial distribution (TISD) based on clarity, a metric that captures the decay of information in the absence of observations and highlights regions of high uncertainty; and (2) we introduce Robustmesch (Rmesch), an online scheduling method that enables persistent operation by coordinating rechargeable robots sharing a single mobile charging station. Unlike prior work, our approach avoids reliance on preplanned schedules, static or dedicated charging stations, and simplified robot dynamics. Instead, the scheduler supports general nonlinear models, accounts for uncertainty in the estimated position of the charging station, and handles central node failures. The proposed framework is validated through real‑world hardware experiments, and feasibility guarantees are provided under specific assumptions.

Abstract:
Motion planning problems for physically‑coupled multi‑robot systems in cluttered environments are challenging due to their high dimensionality. Existing methods combining sampling‑based planners with trajectory optimization produce suboptimal results and lack theoretical guarantees. We propose Physically‑coupled discontinuity‑bounded Conflict‑Based Search (pc‑dbCBS), an anytime kinodynamic motion planner, that extends discontinuity‑bounded CBS to rigidly‑coupled systems. Our approach proposes a tri‑level conflict detection and resolution framework that includes the physical coupling between the robots. Moreover, pc‑dbCBS alternates iteratively between state space representations, thereby preserving probabilistic completeness and asymptotic optimality while relying only on single‑robot motion primitives. Across 25 simulated and six real‑world problems involving multirotors carrying a cable‑suspended payload and differential‑drive robots linked by rigid rods, pc‑dbCBS solves up to 92% more instances than a state‑of‑the‑art baseline and plans trajectories that are 50‑60% faster while reducing planning time by an order of magnitude.

Abstract:
In this paper, a novel framework is presented that achieves a combined solution based on Multi‑Robot Task Allocation (MRTA) and collision avoidance with respect to homogeneous measurement tasks taking place in industrial environments. The spatial clustering we propose offers to simultaneously solve the task allocation problem and deal with collision risks by cutting the workspace into distinguishable operational zones for each robot. To divide task sites and to schedule robot routes within corresponding clusters, we use K‑means clustering and the 2‑Opt algorithm. The presented framework shows satisfactory performance, where up to 93% time reduction (1.24s against 17.62s) with a solution quality improvement of up to 7% compared to the best performing method is demonstrated. Our method also completely eliminates collision points that persist in comparative methods in a most significant sense. Theoretical analysis agrees with the claim that spatial partitioning unifies the apparently disjoint tasks allocation and collision avoidance problems under conditions of many identical tasks to be distributed over sparse geographical areas. Ultimately, the findings in this work are of substantial importance for real world applications where both computational efficiency and operation free from collisions is of paramount importance.

Abstract:
Formation control is essential for swarm robotics, enabling coordinated behavior in complex environments. In this paper, we introduce a novel formation control system for an indoor blimp swarm using a specialized leader‑follower approach enhanced with a dynamic leader‑switching mechanism. This strategy allows any blimp to take on the leader role, distributing maneuvering demands across the swarm and enhancing overall formation stability. Only the leader blimp is manually controlled by a human operator, while follower blimps use onboard monocular cameras and a laser altimeter for relative position and altitude estimation. A leader‑switching scheme is proposed to assist the human operator to maintain stability of the swarm, especially when a sharp turn is performed. Experimental results confirm that the leader‑switching mechanism effectively maintains stable formations and adapts to dynamic indoor environments while assisting human operator.

Abstract:
Coordinating time‑sensitive deliveries in environments like hospitals poses a complex challenge, particularly when managing multiple online pickup and delivery requests within strict time windows using a team of heterogeneous robots. Traditional approaches fail to address dynamic rescheduling or diverse service requirements, typically restricting robots to single‑task types. This paper tackles the Multi‑Pickup and Delivery Problem with Time Windows (MPDPTW), where autonomous mobile robots are capable of handling varied service requests. The objective is to minimize late delivery penalties while maximizing task completion rates. To achieve this, we propose a novel framework leveraging a heterogeneous robot team and an efficient dynamic scheduling algorithm that supports dynamic task rescheduling. Users submit requests with specific time constraints, and our decentralized algorithm, Heterogeneous Mobile Robots Online Diverse Task Allocation (HMR‑ODTA), optimizes task assignments to ensure timely service while addressing delays or task rejections. Extensive simulations validate the algorithm's effectiveness. For smaller task sets (40‑160 tasks), penalties were reduced by nearly 63%, while for larger sets (160‑280 tasks), penalties decreased by approximately 50%. These results highlight the algorithm's effectiveness in improving task scheduling and coordination in multi‑robot systems, offering a robust solution for enhancing delivery performance in structured, time‑critical environments.

Abstract:
Reliable perception remains a key challenge for Connected Automated Vehicles (CAVs) in complex real‑world environments, where varying lighting conditions and adverse weather degrade sensing performance. While existing multi‑sensor solutions improve local robustness, they remain constrained by limited sensing range, line‑of‑sight occlusions, and sensor failures on individual vehicles. This paper introduces VALISENS, a validated cooperative perception system that extends multi‑sensor fusion beyond a single vehicle through Vehicle‑to‑Everything (V2X)‑enabled collaboration between Connected Automated Vehicles (CAVs) and intelligent infrastructure. VALISENS integrates onboard and roadside LiDARs, radars, RGB cameras, and thermal cameras within a unified multi‑agent perception framework. Thermal cameras enhances the detection of Vulnerable Road Users (VRUs) under challenging lighting conditions, while roadside sensors reduce occlusions and expand the effective perception range. In addition, an integrated sensor monitoring module continuously assesses sensor health and detects anomalies before system degradation occurs. The proposed system is implemented and evaluated in a dedicated real‑world testbed. Experimental results show that VALISENS improves pedestrian situational awareness by up to 18% compared with vehicle‑only sensing, while the sensor monitoring module achieves over 97% accuracy, demonstrating its effectiveness and its potential to support future Cooperative Intelligent Transport Systems (C‑ITS) applications.

Abstract:
Large Language Models (LLMs) have advanced rapidly in recent years, demonstrating strong capabilities in problem comprehension and reasoning. Inspired by these developments, researchers have begun exploring the use of LLMs as decentralized decision‑makers for multi‑robot formation control. However, prior studies reveal that directly applying LLMs to such tasks often leads to unstable and inconsistent behaviors, where robots may collapse to the centroid of their positions or diverge entirely due to hallucinated reasoning, logical inconsistencies, and limited coordination awareness. To overcome these limitations, we propose a novel framework that integrates LLMs with an influence‑based plan consensus protocol. In this framework, each robot independently generates a local plan toward the desired formation using its own LLM. The robots then iteratively refine their plans through a decentralized consensus protocol that accounts for their influence on neighboring robots. This process drives the system toward a coherent and stable flocking formation in a fully decentralized manner. We evaluate our approach through comprehensive simulations involving both state‑of‑the‑art closed‑source LLMs (e.g., o3‑mini, Claude 3.5) and open‑source models (e.g., Llama3.1‑405b, Qwen‑Max, DeepSeek‑R1). The results show notable improvements in stability, convergence, and adaptability over previous LLM‑based methods. We further validate our framework on a physical team of Crazyflie drones, demonstrating its practical viability and effectiveness in real‑world multi‑robot systems.

Abstract:
Autonomous landing is essential for drones deployed in emergency deliveries, post‑disaster response, and other large‑scale missions. By enabling self‑docking on charging platforms, it facilitates continuous operation and significantly extends mission endurance. However, traditional approaches often fall short in dynamic, unstructured environments due to limited semantic awareness and reliance on fixed, context‑insensitive safety margins. To address these limitations, we propose a hybrid framework that integrates large language model (LLMs) with model predictive control (MPC). Our approach begins with a vision‑language encoder (VLE) (e.g., BLIP), which transforms real‑time images into concise textual scene descriptions. These descriptions are processed by a lightweight LLM (e.g., Qwen 2.5 1.5B or LLaMA 3.2 1B) equipped with retrieval‑augmented generation (RAG) to classify scene elements and infer context‑aware safety buffers, such as 3 meters for pedestrians and 5 meters for vehicles. The resulting semantic flags and unsafe regions are then fed into an MPC module, enabling real‑time trajectory replanning that avoids collisions while maintaining high landing precision. We validate our framework in the ROS‑Gazebo simulator, where it consistently outperforms conventional vision‑based MPC baselines. Our results show a significant reduction in near‑miss incidents with dynamic obstacles, while preserving accurate landings in cluttered environments.

Abstract:
In this work, we fabricated and studied two designs for omnidirectional vision sensors for swarm robotics, based on catadioptric systems consisting of a mirror with rotational symmetry, eight discrete infrared photodiodes and a single LED, in order to provide localization and navigation abilities for mobile robotic agents. We considered two arrangements for the photodiodes: one in which they point upward into the mirror, and one in which they point outward, perpendicular to the mirror. To determine which design offers a better field of view on the plane, as well as detection of distance and orientation between two agents, we developed a test rail with three degrees of freedom to experimentally and systematically measure the signal registered by the photodiodes of a given sensor (in a single readout) from the light emitted by another as functions of the distance and orientation. Afterwards, we processed and analyzed the experimental data to develop mathematical models for the mean response of a photodiode in each design. Finally, by numerically inverting the models, we compared the two designs in terms of their accuracy. Our results show that the design with the photodiodes pointing upward resolves better the distance, while the other resolves better the orientation of the emitting agent, both providing an omnidirectional field of view.

Abstract:
Cooperative perception, leveraging shared information from multiple vehicles via vehicle‑to‑vehicle (V2V) communication, plays a vital role in autonomous driving to alleviate the limitation of single‑vehicle perception. Existing works have explored the effects of V2V communication impairments on perception precision, but they lack generalization to different levels of impairments. In this work, we propose a joint weighting and denoising framework, Coop‑WD, to enhance cooperative perception subject to V2V channel impairments. In this framework, the self‑supervised contrastive model and the conditional diffusion probabilistic model are adopted hierarchically for vehicle‑level and pixel‑level feature enhancement. An efficient variant model, Coop‑WD‑eco, is proposed to selectively deactivate denoising to reduce processing overhead. Rician fading, non‑stationarity, and time‑varying distortion are considered. Simulation results demonstrate that the proposed Coop‑WD outperforms conventional benchmarks in all types of channels. Qualitative analysis with visual examples further proves the superiority of our proposed method. The proposed Coop‑WD‑eco achieves up to 50% reduction in computational cost under severe distortion while maintaining comparable accuracy as channel conditions improve.

Abstract:
Robot swarms navigating through unknown obstacle environments are an emerging research area that faces challenges. Performing tasks in such environments requires swarms to achieve autonomous localization, perception, decision‑making, control, and planning. The limited computational resources of onboard platforms present significant challenges for planning and control. Reactive planners offer low computational demands and high re‑planning frequencies but lack predictive capabilities, often resulting in local minima. Multi‑step planners can make multi‑step predictions to reduce deadlocks, but they require substantial computation, resulting in a lower replanning frequency. This paper proposes a novel homotopic trajectory planning framework for a robot swarm that combines centralized homotopic trajectory planning (optimal virtual tube planning) with distributed control, enabling low‑computation, high‑frequency replanning, thereby uniting the strengths of multi‑step and reactive planners. Based on multi‑parametric programming, homotopic optimal trajectories are approximated by affine functions. The resulting approximate solutions have computational complexity O(n_t), where n_t is the number of trajectory parameters. This low complexity makes centralized planning of a large number of optimal trajectories practical and, when combined with distributed control, enables rapid, low‑cost replanning. The effectiveness of the proposed method is validated through several simulations and experiments.

Abstract:
Consistent localization of cooperative multi‑robot systems during navigation presents substantial challenges. This paper proposes a fault‑tolerant, multi‑modal localization framework for multi‑robot systems on matrix Lie groups. We introduce novel stochastic operations to perform composition, differencing, inversion, averaging, and fusion of correlated and non‑correlated estimates on Lie groups, enabling pseudo‑pose construction for filter updates. The method integrates a combination of proprioceptive and exteroceptive measurements from inertial, velocity, and pose (pseudo‑pose) sensors on each robot in an Extended Kalman Filter (EKF) framework. The prediction step is conducted on the Lie group \mathbbSE_2(3) × \mathbbR^3 × \mathbbR^3, where each robot's pose, velocity, and inertial measurement biases are propagated. The proposed framework uses body velocity, relative pose measurements from fiducial markers, and inter‑robot communication to provide scalable EKF update across the network on the Lie group \mathbbSE(3) × \mathbbR^3. A fault detection module is implemented, allowing the integration of only reliable pseudo‑pose measurements from fiducial markers. We demonstrate the effectiveness of the method through experiments with a network of wheeled mobile robots equipped with inertial measurement units, wheel odometry, and ArUco markers. The comparison results highlight the proposed method's real‑time performance, superior efficiency, reliability, and scalability in multi‑robot localization, making it well‑suited for large‑scale robotic systems.

Abstract:
Rapid advancements in artificial intelligence (AI) have enabled robots to performcomplex tasks autonomously with increasing precision. However, multi‑robot systems (MRSs) face challenges in generalization, heterogeneity, and safety, especially when scaling to large‑scale deployments like disaster response. Traditional approaches often lack generalization, requiring extensive engineering for new tasks and scenarios, and struggle with managing diverse robots. To overcome these limitations, we propose a Human‑in‑the‑loop Multi‑Robot Collaboration Framework (HMCF) powered by large language models (LLMs). LLMs enhance adaptability by reasoning over diverse tasks and robot capabilities, while human oversight ensures safety and reliability, intervening only when necessary. Our framework seamlessly integrates human oversight, LLM agents, and heterogeneous robots to optimize task allocation and execution. Each robot is equipped with an LLM agent capable of understanding its capabilities, converting tasks into executable instructions, and reducing hallucinations through task verification and human supervision. Simulation results show that our framework outperforms state‑of‑the‑art task planning methods, achieving higher task success rates with an improvement of 4.76%. Real‑world tests demonstrate its robust zero‑shot generalization feature and ability to handle diverse tasks and environments with minimal human intervention.

Abstract:
Cooperative perception extends the perception capabilities of autonomous vehicles by enabling multi‑agent information sharing via Vehicle‑to‑Everything (V2X) communication. Unlike traditional onboard sensors, V2X acts as a dynamic "information sensor" characterized by limited communication, heterogeneity, mobility, and scalability. This survey provides a comprehensive review of recent advancements from the perspective of information‑centric cooperative perception, focusing on three key dimensions: information representation, information fusion, and large‑scale deployment. We categorize information representation into data‑level, feature‑level, and object‑level schemes, and highlight emerging methods for reducing data volume and compressing messages under communication constraints. In information fusion, we explore techniques under both ideal and non‑ideal conditions, including those addressing heterogeneity, localization errors, latency, and packet loss. Finally, we summarize system‑level approaches to support scalability in dense traffic scenarios. Compared with existing surveys, this paper introduces a new perspective by treating V2X communication as an information sensor and emphasizing the challenges of deploying cooperative perception in real‑world intelligent transportation systems.

Abstract:
We propose an opinion‑driven navigation framework for multi‑robot traversal through a narrow corridor. Our approach leverages a multi‑agent decision‑making model known as the Nonlinear Opinion Dynamics (NOD) to address the narrow corridor passage problem, formulated as a multi‑robot navigation game. By integrating the NOD model with a multi‑robot path planning algorithm, we demonstrate that the framework effectively reduces the likelihood of deadlocks during corridor traversal. To ensure scalability with an increasing number of robots, we introduce a game reduction technique that enables efficient coordination in larger groups. Extensive simulation studies are conducted to validate the effectiveness of the proposed approach.

Abstract:
GenGrid is a novel comprehensive open‑source, distributed platform intended for conducting extensive swarm robotic experiments. The modular platform is designed to run swarm robotics experiments that are compatible with different types of mobile robots ranging from Colias, Kilobot, and E puck. The platform offers programmable control over the experimental setup and its parameters and acts as a tool to collect swarm robot data, including localization, sensory feedback, messaging, and interaction. GenGrid is designed as a modular grid of attachable computing nodes that offers bidirectional communication between the robotic agent and grid nodes and within grids. The paper describes the hardware and software architecture design of the GenGrid system. Further, it discusses some common experimental studies covering multi‑robot and swarm robotics to showcase the platform's use. GenGrid of 25 homogeneous cells with identical sensing and communication characteristics with a footprint of 37.5 cm X 37.5 cm, exhibits multiple capabilities with minimal resources. The open‑source hardware platform is handy for running swarm experiments, including robot hopping based on multiple gradients, collective transport, shepherding, continuous pheromone deposition, and subsequent evaporation. The low‑cost, modular, and open‑source platform is significant in the swarm robotics research community, which is currently driven by commercial platforms that allow minimal modifications.

Abstract:
Task and motion planning (TAMP) for multi‑robot systems, which integrates discrete task planning with continuous motion planning, remains a challenging problem in robotics. Existing TAMP approaches often struggle to scale effectively for multi‑robot systems with complex specifications, leading to infeasible solutions and prolonged computation times. This work addresses the TAMP problem in multi‑robot settings where tasks are specified using expressive hierarchical temporal logic and task assignments are not pre‑determined. Our approach leverages the efficiency of hierarchical temporal logic specifications for task‑level planning and the optimization‑based graph of convex sets method for motion‑level planning, integrating them within a product graph framework. At the task level, we convert hierarchical temporal logic specifications into a single graph, embedding task allocation within its edges. At the motion level, we represent the feasible motions of multiple robots through convex sets in the configuration space, guided by a sampling‑based motion planner. This formulation allows us to define the TAMP problem as a shortest path search within the product graph, where efficient convex optimization techniques can be applied. We prove that our approach is both sound and complete under mild assumptions. Additionally, we extend our framework to cooperative pick‑and‑place tasks involving object handovers between robots. We evaluate our method across various high‑dimensional multi‑robot scenarios, including simulated and real‑world environments with quadrupeds, robotic arms, and automated conveyor systems. Our results show that our approach outperforms existing methods in execution time and solution optimality while effectively scaling with task complexity.

Abstract:
In swarm robotics, confrontation scenarios, including strategic confrontations, require efficient decision‑making that integrates discrete commands and continuous actions. Traditional task and motion planning methods separate decision‑making into two layers, but their unidirectional structure fails to capture the interdependence between these layers, limiting adaptability in dynamic environments. Here, we propose a novel bidirectional approach based on hierarchical reinforcement learning, enabling dynamic interaction between the layers. This method effectively maps commands to task allocation and actions to path planning, while leveraging cross‑training techniques to enhance learning across the hierarchical framework. Furthermore, we introduce a trajectory prediction model that bridges abstract task representations with actionable planning goals. In our experiments, it achieves over 80% in confrontation win rate and under 0.01 seconds in decision time, outperforming existing approaches. Demonstrations through large‑scale tests and real‑world robot experiments further emphasize the generalization capabilities and practical applicability of our method.

Abstract:
Tasks for multi‑robot systems often require the robots to collaborate and complete a team goal while maintaining safety. This problem is usually formalized as a constrained Markov decision process (CMDP), which targets minimizing a global cost and bringing the mean of constraint violation below a user‑defined threshold. Inspired by real‑world robotic applications, we define safety as zero constraint violation. While many safe multi‑agent reinforcement learning (MARL) algorithms have been proposed to solve CMDPs, these algorithms suffer from unstable training in this setting. To tackle this, we use the epigraph form for constrained optimization to improve training stability and prove that the centralized epigraph form problem can be solved in a distributed fashion by each agent. This results in a novel centralized training distributed execution MARL algorithm named Def‑MARL. Simulation experiments on 8 different tasks across 2 different simulators show that Def‑MARL achieves the best overall performance, satisfies safety constraints, and maintains stable training. Real‑world hardware experiments on Crazyflie quadcopters demonstrate the ability of Def‑MARL to safely coordinate agents to complete complex collaborative tasks compared to other methods.

Abstract:
This paper introduces MRTA‑Sim, a Python/ROS2/Gazebo simulator for testing approaches to Multi‑Robot Task Allocation (MRTA) problems on simulated robots in complex, indoor environments. Grid‑based approaches to MRTA problems can be too restrictive for use in complex, dynamic environments such in warehouses, department stores, hospitals, etc. However, approaches that operate in free‑space often operate at a layer of abstraction above the control and planning layers of a robot and make an assumption on approximate travel time between points of interest in the system. These abstractions can neglect the impact of the tight space and multi‑agent interactions on the quality of the solution. Therefore, MRTA solutions should be tested with the navigation stacks of the robots in mind, taking into account robot planning, conflict avoidance between robots, and human interaction and avoidance. This tool connects the allocation output of MRTA solvers to individual robot planning using the NAV2 stack and local, centralized multi‑robot deconfliction using Control Barrier Function‑Quadrtic Programs (CBF‑QPs), creating a platform closer to real‑world operation for more comprehensive testing of these approaches. The simulation architecture is modular so that users can swap out methods at different levels of the stack. We show the use of our system with a Satisfiability Modulo Theories (SMT)‑based approach to dynamic MRTA on a fleet of indoor delivery robots.

Abstract:
Multi‑agent systems seeking consensus may also have other objective functions to optimize, requiring the research of multi‑objective optimization in consensus. Several recent publications have explored this domain using various methods such as weighted‑sum optimization and penalization methods. This paper reviews the state of the art for consensus‑based multi‑objective optimization, poses a multi‑agent lunar rover exploration problem seeking consensus and maximization of explored area, and achieves optimal edge weights and steering angles by applying SQP algorithms.

Abstract:
This paper describes the Pogobot, an open‑source platform specifically designed for research at the interface of swarm robotics and active matter. Pogobot features vibration‑based or wheel‑based locomotion, fast infrared communication, and an array of sensors in a cost‑effective package (approx. 250euros/unit). The platform's modular design, comprehensive API, and extensible architecture facilitate the implementation of swarm intelligence algorithms and collective motion. Pogobots offer an accessible alternative to existing platforms while providing advanced capabilities including directional communication between units and fast locomotion, all with a compact form factor. More than 200 Pogobots are already being used on a daily basis in several Universities to study self‑organizing systems, programmable active matter, discrete reaction‑diffusion‑advection systems and computational models of social learning and evolution. This paper details the hardware and software architecture, communication protocols, locomotion mechanisms, and the infrastructure built around the Pogobots.

Abstract:
Infrastructure sensing is vital for traffic monitoring at safety hotspots (e.g., intersections) and serves as the backbone of cooperative perception in autonomous driving. While vehicle sensing has been extensively studied, infrastructure sensing has received little attention, especially given the unique challenges of diverse intersection geometries, complex occlusions, varying traffic conditions, and ambient environments like lighting and weather. To address these issues and ensure cost‑effective sensor placement, we propose Heterogeneous Multi‑Modal Infrastructure Sensor Placement Evaluation (InSPE), a perception surrogate metric set that rapidly assesses perception effectiveness across diverse infrastructure and environmental scenarios with combinations of multi‑modal sensors. InSPE systematically evaluates perception capabilities by integrating three carefully designed metrics, i.e., sensor coverage, perception occlusion, and information gain. To support large‑scale evaluation, we develop a data generation tool within the CARLA simulator and also introduce Infra‑Set, a dataset covering diverse intersection types and environmental conditions. Benchmarking experiments with state‑of‑the‑art perception algorithms demonstrate that InSPE enables efficient and scalable sensor placement analysis, providing a robust solution for optimizing intelligent intersection infrastructure.

Abstract:
Perception is a key component of Automated vehicles (AVs). However, sensors mounted to the AVs often encounter blind spots due to obstructions from other vehicles, infrastructure, or objects in the surrounding area. While recent advancements in planning and control algorithms help AVs react to sudden object appearances from blind spots at low speeds and less complex scenarios, challenges remain at high speeds and complex intersections. Vehicle to Infrastructure (V2I) technology promises to enhance scene representation for AVs in complex intersections, providing sufficient time and distance to react to adversary vehicles violating traffic rules. Most existing methods for infrastructure‑based vehicle detection and tracking rely on LIDAR, RADAR or sensor fusion methods, such as LIDAR‑Camera and RADAR‑Camera. Although LIDAR and RADAR provide accurate spatial information, the sparsity of point cloud data limits its ability to capture detailed object contours of objects far away, resulting in inaccurate 3D object detection results. Furthermore, the absence of LIDAR or RADAR at every intersection increases the cost of implementing V2I technology. To address these challenges, this paper proposes a V2I framework that utilizes monocular traffic cameras at road intersections to detect 3D objects. The results from the roadside unit (RSU) are then combined with the on‑board system using an asynchronous late fusion method to enhance scene representation. Additionally, the proposed framework provides a time delay compensation module to compensate for the processing and transmission delay from the RSU. Lastly, the V2I framework is tested by simulating and validating a scenario similar to the one described in an industry report by Waymo. The results show that the proposed method improves the scene representation and the AV's perception range, giving enough time and space to react to adversary vehicles.

Abstract:
The collective perception problem ‑‑ where a group of robots perceives its surroundings and comes to a consensus on an environmental state ‑‑ is a fundamental problem in swarm robotics. Past works studying collective perception use either an entire robot swarm with perfect sensing or a swarm with only a handful of malfunctioning members. A related study proposed an algorithm that does account for an entire swarm of unreliable robots but assumes that the sensor faults are known and remain constant over time. To that end, we build on that study by proposing the Bayes Collective Perception Filter (BayesCPF) that enables robots with continuously degrading sensors to accurately estimate the fill ratio ‑‑ the rate at which an environmental feature occurs. Our main contribution is the Extended Kalman Filter within the BayesCPF, which helps swarm robots calibrate for their time‑varying sensor degradation. We validate our method across different degradation models, initial conditions, and environments in simulated and physical experiments. Our findings show that, regardless of degradation model assumptions, fill ratio estimation using the BayesCPF is competitive to the case if the true sensor accuracy is known, especially when assumptions regarding the model and initial sensor accuracy levels are preserved.

Abstract:
For safe and flexible navigation in multi‑robot systems, this paper presents an enhanced and predictive sampling‑based trajectory planning approach in complex environments, the Gradient Field‑based Dynamic Window Approach (GF‑DWA). Building upon the dynamic window approach, the proposed method utilizes gradient information of obstacle distances as a new cost term to anticipate potential collisions. This enhancement enables the robot to improve awareness of obstacles, including those with non‑convex shapes. The gradient field is derived from the Gaussian process distance field, which generates both the distance field and gradient field by leveraging Gaussian process regression to model the spatial structure of the environment. Through several obstacle avoidance and fleet collision avoidance scenarios, the proposed GF‑DWA is shown to outperform other popular trajectory planning and control methods in terms of safety and flexibility, especially in complex environments with non‑convex obstacles.

Abstract:
This paper addresses the problem of distributed coordination control for multi‑robot systems (MRSs) in the presence of localization uncertainty using a Linear Quadratic Gaussian (LQG) approach. We introduce a stochastic LQG control strategy that ensures the coordination of mobile robots while optimizing a performance criterion. The proposed control framework accounts for the inherent uncertainty in localization measurements, enabling robust decision‑making and coordination. We analyze the stability of the system under the proposed control protocol, deriving conditions for the convergence of the multi‑robot network. The effectiveness of the proposed approach is demonstrated through experimental validation using Robotrium simulation experiments, showcasing the practical applicability of the control strategy in real‑world scenarios with localization uncertainty.

Abstract:
Ensuring resilient consensus in multi‑robot systems with misbehaving agents remains a challenge, as many existing network resilience properties are inherently combinatorial and globally defined. While previous works have proposed control laws to enhance or preserve resilience in multi‑robot networks, they often assume a fixed topology with known resilience properties, or require global state knowledge. These assumptions may be impractical in physically‑constrained environments, where safety and resilience requirements are conflicting, or when misbehaving agents share inaccurate state information. In this work, we propose a distributed control law that enables each robot to guarantee resilient consensus and safety during its navigation without fixed topologies using only locally available information. To this end, we establish a sufficient condition for resilient consensus in time‑varying networks based on the degree of non‑misbehaving or normal agents. Using this condition, we design a Control Barrier Function (CBF)‑based controller that guarantees resilient consensus and collision avoidance without requiring estimates of global state and/or control actions of all other robots. Finally, we validate our method through simulations.

Abstract:
This paper presents field‑tested use cases from Search and Rescue (SAR) missions, highlighting the co‑design of mobile robots and communication systems to support Edge‑Cloud architectures based on 5G Standalone (SA). The main goal is to contribute to the effective cooperation of multiple robots and first responders. Our field experience includes the development of Hybrid Wireless Sensor Networks (H‑WSNs) for risk and victim detection, smartphones integrated into the Robot Operating System (ROS) as Edge devices for mission requests and path planning, real‑time Simultaneous Localization and Mapping (SLAM) via Multi‑Access Edge Computing (MEC), and implementation of Uncrewed Ground Vehicles (UGVs) for victim evacuation in different navigation modes. These experiments, conducted in collaboration with actual first responders, underscore the need for intelligent network resource management, balancing low‑latency and high‑bandwidth demands. Network slicing is key to ensuring critical emergency services are performed despite challenging communication conditions. The paper identifies architectural needs, lessons learned, and challenges to be addressed by 6G technologies to enhance emergency response capabilities.

Abstract:
Many modern robotic systems such as multi‑robot systems and manipulators exhibit redundancy, a property owing to which they are capable of executing multiple tasks. This work proposes a novel method, based on the Reinforcement Learning (RL) paradigm, to train redundant robots to be able to execute multiple tasks concurrently. Our approach differs from typical multi‑objective RL methods insofar as the learned tasks can be combined and executed in possibly time‑varying prioritized stacks. We do so by first defining a notion of task independence between learned value functions. We then use our definition of task independence to propose a cost functional that encourages a policy, based on an approximated value function, to accomplish its control objective while minimally interfering with the execution of higher priority tasks. This allows us to train a set of control policies that can be executed simultaneously. We also introduce a version of fitted value iteration to learn to approximate our proposed cost functional efficiently. We demonstrate our approach on several scenarios and robotic systems.

Abstract:
Multi‑robot coordination is fundamental to various applications, including autonomous exploration, search and rescue, and cooperative transportation. This paper presents an optimal consensus framework for multi‑robot systems (MRSs) that ensures efficient rendezvous while minimizing energy consumption and addressing actuator constraints. A critical challenge in real‑world deployments is actuator limitations, particularly wheel velocity saturation, which can significantly degrade control performance. To address this issue, we incorporate Pontryagin Minimum Principle (PMP) into the control design, facilitating constrained optimization while ensuring system stability and feasibility. The resulting optimal control policy effectively balances coordination efficiency and energy consumption, even in the presence of actuation constraints. The proposed framework is validated through extensive numerical simulations and real‑world experiments conducted using a team of Robotarium mobile robots. The experimental results confirm that our control strategies achieve reliable and efficient coordinated rendezvous while addressing real‑world challenges such as communication delays, sensor noise, and packet loss.

Abstract:
Cooperative perception presents significant potential for enhancing the sensing capabilities of individual vehicles, however, inter‑agent latency remains a critical challenge. Latencies cause misalignments in both spatial and semantic features, complicating the fusion of real‑time observations from the ego vehicle with delayed data from others. To address these issues, we propose TraF‑Align, a novel framework that learns the flow path of features by predicting the feature‑level trajectory of objects from past observations up to the ego vehicle's current time. By generating temporally ordered sampling points along these paths, TraF‑Align directs attention from the current‑time query to relevant historical features along each trajectory, supporting the reconstruction of current‑time features and promoting semantic interaction across multiple frames. This approach corrects spatial misalignment and ensures semantic consistency across agents, effectively compensating for motion and achieving coherent feature fusion. Experiments on two real‑world datasets, V2V4Real and DAIR‑V2X‑Seq, show that TraF‑Align sets a new benchmark for asynchronous cooperative perception.

Abstract:
Autonomous Vehicles (AVs) rely on individual perception systems to navigate safely. However, these systems face significant challenges in adverse weather conditions, complex road geometries, and dense traffic scenarios. Cooperative Perception (CP) has emerged as a promising approach to extending the perception quality of AVs by jointly processing shared camera feeds and sensor readings across multiple vehicles. This work presents a novel CP framework designed to optimize vehicle selection and networking resource utilization under imperfect communications. Our optimized CP formation considers critical factors such as the helper vehicles' spatial position, visual range, motion blur, and available communication budgets. Furthermore, our resource optimization module allocates communication channels while adjusting power levels to maximize data flow efficiency between the ego and helper vehicles, considering realistic models of modern vehicular communication systems, such as LTE and 5G NR‑V2X. We validate our approach through extensive experiments on pedestrian detection in challenging scenarios, using synthetic data generated by the CARLA simulator. The results demonstrate that our method significantly improves upon the perception quality of individual AVs with about 10% gain in detection accuracy. This substantial gain uncovers the unleashed potential of CP to enhance AV safety and performance in complex situations.

Abstract:
LiDAR‑based Vehicle‑to‑Everything (V2X) cooperative perception has demonstrated its impact on the safety and effectiveness of autonomous driving. Since current cooperative perception algorithms are trained and tested on the same dataset, the generalization ability of cooperative perception systems remains underexplored. This paper is the first work to study the Domain Generalization problem of LiDAR‑based V2X cooperative perception (V2X‑DG) for 3D detection based on four widely‑used open source datasets: OPV2V, V2XSet, V2V4Real and DAIR‑V2X. Our research seeks to sustain high performance not only within the source domain but also across other unseen domains, achieved solely through training on source domain. To this end, we propose Cooperative Mixup Augmentation based Generalization (CMAG) to improve the model generalization capability by simulating the unseen cooperation, which is designed compactly for the domain gaps in cooperative perception. Furthermore, we propose a constraint for the regularization of the robust generalized feature representation learning: Cooperation Feature Consistency (CFC), which aligns the intermediately fused features of the generalized cooperation by CMAG and the early fused features of the original cooperation in source domain. Extensive experiments demonstrate that our approach achieves significant performance gains when generalizing to other unseen datasets while it also maintains strong performance on the source dataset.

Abstract:
This paper presents a novel hierarchical, safety‑critical control framework that integrates distributed nonlinear model predictive controllers (DNMPCs) with control barrier functions (CBFs) to enable cooperative locomotion of multi‑agent quadrupedal robots in complex environments. While NMPC‑based methods are widely adopted for enforcing safety constraints and navigating multi‑robot systems (MRSs) through intricate environments, ensuring the safety of MRSs requires a formal definition grounded in the concept of invariant sets. CBFs, typically implemented via quadratic programs (QPs) at the planning layer, provide formal safety guarantees. However, their zero‑control horizon limits their effectiveness for extended trajectory planning in inherently unstable, underactuated, and nonlinear legged robot models. Furthermore, the integration of CBFs into real‑time NMPC for sophisticated MRSs, such as quadrupedal robot teams, remains underexplored. This paper develops computationally efficient, distributed NMPC algorithms that incorporate CBF‑based collision safety guarantees within a consensus protocol, enabling longer planning horizons for safe cooperative locomotion under disturbances and rough terrain conditions. The optimal trajectories generated by the DNMPCs are tracked using full‑order, nonlinear whole‑body controllers at the low level. The proposed approach is validated through extensive numerical simulations with up to four Unitree A1 robots and hardware experiments involving two A1 robots subjected to external pushes, rough terrain, and uncertain obstacle information. Comparative analysis demonstrates that the proposed CBF‑based DNMPCs achieve a 27.89% higher success rate than conventional NMPCs without CBF constraints.

Abstract:
With the accelerated development of Industry 4.0, intelligent manufacturing systems increasingly require efficient task allocation and scheduling in multi‑robot systems. However, existing methods rely on domain expertise and face challenges in adapting to dynamic production constraints. Additionally, enterprises have high privacy requirements for production scheduling data, which prevents the use of cloud‑based large language models (LLMs) for solution development. To address these challenges, there is an urgent need for an automated modeling solution that meets data privacy requirements. This study proposes a knowledge‑augmented mixed integer linear programming (MILP) automated formulation framework, integrating local LLMs with domain‑specific knowledge bases to generate executable code from natural language descriptions automatically. The framework employs a knowledge‑guided DeepSeek‑R1‑Distill‑Qwen‑32B model to extract complex spatiotemporal constraints (82% average accuracy) and leverages a supervised fine‑tuned Qwen2.5‑Coder‑7B‑Instruct model for efficient MILP code generation (90% average accuracy). Experimental results demonstrate that the framework successfully achieves automatic modeling in the aircraft skin manufacturing case while ensuring data privacy and computational efficiency. This research provides a low‑barrier and highly reliable technical path for modeling in complex industrial scenarios.

Abstract:
Cooperative perception can increase the view field and decrease the occlusion of an ego vehicle, hence improving the perception performance and safety of autonomous driving. Despite the success of previous works on cooperative object detection, they mostly operate on dense Bird's Eye View (BEV) feature maps, which are computationally demanding and can hardly be extended to long‑range detection problems. More efficient fully sparse frameworks are rarely explored. In this work, we design a fully sparse framework, SparseAlign, with three key features: an enhanced sparse 3D backbone, a query‑based temporal context learning module, and a robust detection head specially tailored for sparse features. Extensive experimental results on both OPV2V and DairV2X datasets show that our framework, despite its sparsity, outperforms the state of the art with less communication bandwidth requirements. In addition, experiments on the OPV2Vt and DairV2Xt datasets for time‑aligned cooperative object detection also show a significant performance gain compared to the baseline works.

Abstract:
Multi‑robot autonomous exploration in an unknown environment is an important application in robotics.Traditional exploration methods only use information around frontier points or viewpoints, ignoring spatial information of unknown areas. Moreover, finding the exact optimal solution for multi‑robot task allocation is NP‑hard, resulting in significant computational time consumption. To address these issues, we present a hierarchical multi‑robot exploration framework using a new modeling method called RegionGraph. The proposed approach makes two main contributions: 1) A new modeling method for unexplored areas that preserves their spatial information across the entire space in a weighted graph called RegionGraph. 2) A hierarchical multi‑robot exploration framework that decomposes the global exploration task into smaller subtasks, reducing the frequency of global planning and enabling asynchronous exploration. The proposed method is validated through both simulation and real‑world experiments, demonstrating a 20% improvement in efficiency compared to existing methods.

Abstract:
This work proposes a novel multi‑robot task allocation framework for robots that can switch between multiple modes, e.g., flying, driving, or walking. We first provide a method to encode the multi‑mode property of robots as a graph, where the mode of each robot is represented by a node. Next, we formulate a constrained optimization problem to decide both the task to be allocated to each robot as well as the mode in which the latter should execute the task. The robot modes are optimized based on the state of the robot and the environment, as well as the energy required to execute the allocated task. Moreover, the proposed framework is able to encompass kinematic and dynamic models of robots alike. Furthermore, we provide sufficient conditions for the convergence of task execution and allocation for both robot models.

Abstract:
We introduce a novel distributed source seeking framework, DIAS, designed for multi‑robot systems in scenarios where the number of sources is unknown and potentially exceeds the number of robots. Traditional robotic source seeking methods typically focused on directing each robot to a specific strong source and may fall short in comprehensively identifying all potential sources. DIAS addresses this gap by introducing a hybrid controller that identifies the presence of sources and then alternates between exploration for data gathering and exploitation for guiding robots to identified sources. It further enhances search efficiency by dividing the environment into Voronoi cells and approximating source density functions based on Gaussian process regression. Additionally, DIAS can be integrated with existing source seeking algorithms. We compare DIAS with existing algorithms, including DoSS and GMES in simulated gas leakage scenarios where the number of sources outnumbers or is equal to the number of robots. The numerical results show that DIAS outperforms the baseline methods in both the efficiency of source identification by the robots and the accuracy of the estimated environmental density function.

Abstract:
Cooperative perception enabled by Vehicle‑to‑Everything (V2X) communication holds significant promise for enhancing the perception capabilities of autonomous vehicles, allowing them to overcome occlusions and extend their field of view. However, existing research predominantly relies on simulated environments or static datasets, leaving the feasibility and effectiveness of V2X cooperative perception especially for intermediate fusion in real‑world scenarios largely unexplored. In this work, we introduce V2X‑ReaLO, an open online cooperative perception framework deployed on real vehicles and smart infrastructure that integrates early, late, and intermediate fusion methods within a unified pipeline and provides the first practical demonstration of online intermediate fusion's feasibility and performance under genuine real‑world conditions. Additionally, we present an open benchmark dataset specifically designed to assess the performance of online cooperative perception systems. This new dataset extends V2X‑Real dataset to dynamic, synchronized ROS bags and provides 25,028 test frames with 6,850 annotated key frames in challenging urban scenarios. By enabling real‑time assessments of perception accuracy and communication lantency under dynamic conditions, V2X‑ReaLO sets a new benchmark for advancing and optimizing cooperative perception systems in real‑world applications. The codes and datasets will be released to further advance the field.

Abstract:
In this paper, we propose a framework, collective behavioral cloning (CBC), to learn the underlying interaction mechanism and control policy of a swarm system. Given the trajectory data of a swarm system, we propose a graph variational autoencoder (GVAE) to learn the local interaction graph. Based on the interaction graph and swarm trajectory, we use behavioral cloning to learn the control policy of the swarm system. To demonstrate the practicality of CBC, we deploy it on a real‑world decentralized vision‑based robot swarm system. A visual attention network is trained based on the learned interaction graph for online neighbor selection. Experimental results show that our method outperforms previous approaches in predicting both the interaction graph and swarm actions with higher accuracy. This work offers a promising approach for understanding interaction mechanisms and swarm dynamics in future swarm robotics research. Code and data are available.

Abstract:
We propose a hybrid approach for decentralized multi‑robot navigation that ensures both safety and deadlock prevention. Building on a standard control formulation, we add a lightweight deadlock prevention mechanism by forming temporary "roundabouts" (circular reference paths). Each robot relies only on local, peer‑to‑peer communication and a controller for base collision avoidance; a roundabout is generated or joined on demand to avert deadlocks. Robots in the roundabout travel in one direction until an escape condition is met, allowing them to return to goal‑oriented motion. Unlike classical decentralized methods that lack explicit deadlock resolution, our roundabout maneuver ensures system‑wide forward progress while preserving safety constraints. Extensive simulations and physical robot experiments show that our method consistently outperforms or matches the success and arrival rates of other decentralized control approaches, particularly in cluttered or high‑density scenarios, all with minimal centralized coordination.

Abstract:
Cooling processes in nature are typically generated by external contact with a cold reservoir or bath. According to the laws of thermodynamics, the final temperature of a system is determined by the temperature of the environment. Here, we report a spontaneous internal cooling phenomenon for active particles, occurring without external contact. This effect, termed ``self‑sustained frictional cooling'', arises from the interplay between activity and dry (Coulomb) friction, and in addition is self‑sustained from particles densely caged by their neighbors. If an active particle moves in its cage, dry friction will stop any further motion after a collision with a neighbor particle thus cooling the particle down to an extremely low temperature. We demonstrate and verify this self‑sustained cooling through experiments and simulations on active granular robots and identify dense frictional arrested clusters coexisting with hot, dilute regions. Our findings offer potential applications in two‑dimensional swarm robotics, where activity and dry friction can serve as externally tunable mechanisms to regulate the swarm's dynamical and structural properties.

Abstract:
Swarm robotics plays a crucial role in enabling autonomous operations in dynamic and unpredictable environments. However, a major challenge remains ensuring safe and efficient navigation in environments filled with both dynamic alive (e.g., humans) and dynamic inanimate (e.g., non‑living objects) obstacles. In this paper, we propose ImpedanceGPT, a novel system that combines a Vision‑Language Model (VLM) with retrieval‑augmented generation (RAG) to enable real‑time reasoning for adaptive navigation of mini‑drone swarms in complex environments. The key innovation of ImpedanceGPT lies in the integration of VLM and RAG, which provides the drones with enhanced semantic understanding of their surroundings. This enables the system to dynamically adjust impedance control parameters in response to obstacle types and environmental conditions. Our approach not only ensures safe and precise navigation but also improves coordination between drones in the swarm. Experimental evaluations demonstrate the effectiveness of the system. The VLM‑RAG framework achieved an obstacle detection and retrieval accuracy of 80 % under optimal lighting. In static environments, drones navigated dynamic inanimate obstacles at 1.4 m/s but slowed to 0.7 m/s with increased separation around humans. In dynamic environments, speed adjusted to 1.0 m/s near hard obstacles, while reducing to 0.6 m/s with higher deflection to safely avoid moving humans.

Abstract:
Motion planning with simple objectives, such as collision‑avoidance and goal‑reaching, can be solved efficiently using modern planners. However, the complexity of the allowed tasks for these planners is limited. On the other hand, signal temporal logic (STL) can specify complex requirements, but STL‑based motion planning and control algorithms often face scalability issues, especially in large multi‑robot systems with complex dynamics. In this paper, we propose an algorithm that leverages the best of the two worlds. We first use a single‑robot motion planner to efficiently generate a set of alternative reference paths for each robot. Then coordination requirements are specified using STL, which is defined over the assignment of paths and robots' progress along those paths. We use a Mixed Integer Linear Program (MILP) to compute task assignments and robot progress targets over time such that the STL specification is satisfied. Finally, a local controller is used to track the target progress. Simulations demonstrate that our method can handle tasks with complex constraints and scales to large multi‑robot teams and intricate task allocation scenarios.

Abstract:
In emerging multi‑robot societies, heterogeneous agents must continually extract and integrate local knowledge from one another through communication, even when their internal models are completely opaque. Existing approaches to continual or collaborative learning for visual place recognition (VPR) largely assume white‑box access to model parameters or shared training datasets, which is unrealistic when robots encounter unknown peers in the wild. This paper introduces \emphContinual Communicative Learning (CCL), a data‑free multi‑robot framework in which a traveler robot (student) continually improves its VPR capability by communicating with black‑box teacher models via a constrained query‑‑response channel. We repurpose Membership Inference Attacks (MIA), originally developed as privacy attacks on machine learning models, as a constructive communication primitive to reconstruct pseudo‑training sets from black‑box VPR teachers without accessing their parameters or raw data. To overcome the intrinsic communication bottleneck caused by the low sampling efficiency of black‑box MIA, we propose a prior‑based query strategy that leverages the student's own VPR prior to focus queries on informative regions of the embedding space, thereby reducing the knowledge transfer (KT) cost. Experimental results on a standard multi‑session VPR benchmark demonstrate that the proposed CCL framework yields substantial performance gains for low‑performing robots under modest communication budgets, highlighting CCL as a promising building block for scalable and fault‑tolerant multi‑robot systems. Furthermore, we propose a Distributed Statistic Integration (DSI) framework that theoretically eliminates catastrophic forgetting by efficiently aggregating sufficient statistics from black‑box VPR models while maintaining data privacy and reducing communication overhead to a sample‑invariant constant complexity.

Abstract:
With cooperative perception, autonomous vehicles can wirelessly share sensor data and representations to overcome sensor occlusions, improving situational awareness. Securing such data exchanges is crucial for connected autonomous vehicles. Existing, automated reputation‑based approaches often suffer from a delay between detection and exclusion of misbehaving vehicles, while majority‑based approaches have communication overheads that limits scalability. In this paper, we introduce CATS, a novel automated system that blends together the best traits of reputation‑based and majority‑based detection mechanisms to secure vehicle‑to‑everything (V2X) communications for cooperative perception, while preserving the privacy of cooperating vehicles. Our evaluation with city‑scale simulations on realistic traffic data shows CATS's effectiveness in rapidly identifying and isolating misbehaving vehicles, with a low false negative rate and overheads, proving its suitability for real world deployments.

Abstract:
Designing safety‑critical controllers for acceleration‑controlled unicycle robots is challenging, as control inputs may not appear in the constraints of control Lyapunov functions(CLFs) and control barrier functions (CBFs), leading to invalid controllers. Existing methods often rely on state‑feedback‑based CLFs and high‑order CBFs (HOCBFs), which are computationally expensive to construct and fail to maintain effectiveness in dynamic environments with fast‑moving, nearby obstacles. To address these challenges, we propose constructing velocity obstacle‑based CBFs (VOCBFs) in the velocity space to enhance dynamic collision avoidance capabilities, instead of relying on distance‑based CBFs that require the introduction of HOCBFs. Additionally, by extending VOCBFs using variants of VO, we enable reactive collision avoidance between robots. We formulate a safety‑critical controller for acceleration‑controlled unicycle robots as a mixed‑integer quadratic programming (MIQP), integrating state‑feedback‑based CLFs for navigation and VOCBFs for collision avoidance. To enhance the efficiency of solving the MIQP, we split the MIQP into multiple sub‑optimization problems and employ a decision network to reduce computational costs. Numerical simulations demonstrate that our approach effectively guides the robot to its target while avoiding collisions. Compared to HOCBFs, VOCBFs exhibit significantly improved dynamic obstacle avoidance performance, especially when obstacles are fast‑moving and close to the robot. Furthermore, we extend our method to distributed multi‑robot systems.

Abstract:
Cooperative perception enhances the individual perception capabilities of autonomous vehicles (AVs) by providing a comprehensive view of the environment. However, balancing perception performance and transmission costs remains a significant challenge. Current approaches that transmit region‑level features across agents are limited in interpretability and demand substantial bandwidth, making them unsuitable for practical applications. In this work, we propose CoopDETR, a novel cooperative perception framework that introduces object‑level feature cooperation via object query. Our framework consists of two key modules: single‑agent query generation, which efficiently encodes raw sensor data into object queries, reducing transmission cost while preserving essential information for detection; and cross‑agent query fusion, which includes Spatial Query Matching (SQM) and Object Query Aggregation (OQA) to enable effective interaction between queries. Our experiments on the OPV2V and V2XSet datasets demonstrate that CoopDETR achieves state‑of‑the‑art performance and significantly reduces transmission costs to 1/782 of previous methods.

Abstract:
Vehicle‑to‑Infrastructure (V2I) collaborative perception leverages data collected by infrastructure's sensors to enhance vehicle perceptual capabilities. LiDAR, as a commonly used sensor in cooperative perception, is widely equipped in intelligent vehicles and infrastructure. However, its superior performance comes with a correspondingly high cost. To achieve low‑cost V2I, reducing the cost of LiDAR is crucial. Therefore, we study adopting low‑resolution LiDAR on the vehicle to minimize cost as much as possible. However, simply reducing the resolution of vehicle's LiDAR results in sparse point clouds, making distant small objects even more blurred. Additionally, traditional communication methods have relatively low bandwidth utilization efficiency. These factors pose challenges for us. To balance cost and perceptual accuracy, we propose a new collaborative perception framework, namely LCV2I. LCV2I uses data collected from cameras and low‑resolution LiDAR as input. It also employs feature offset correction modules and regional feature enhancement algorithms to improve feature representation. Finally, we use regional difference map and regional score map to assess the value of collaboration content, thereby improving communication bandwidth efficiency. In summary, our approach achieves high perceptual performance while substantially reducing the demand for high‑resolution sensors on the vehicle. To evaluate this algorithm, we conduct 3D object detection in the real‑world scenario of DAIR‑V2X, demonstrating that the performance of LCV2I consistently surpasses currently existing algorithms.

Abstract:
Vehicle‑to‑everything technologies (V2X) have become an ideal paradigm to extend the perception range and see through the occlusion. Exiting efforts focus on single‑frame cooperative perception, however, how to capture the temporal cue between frames with V2X to facilitate the prediction task even the planning task is still underexplored. In this paper, we introduce the Co‑MTP, a general cooperative trajectory prediction framework with multi‑temporal fusion for autonomous driving, which leverages the V2X system to fully capture the interaction among agents in both history and future domains to benefit the planning. In the history domain, V2X can complement the incomplete history trajectory in single‑vehicle perception, and we design a heterogeneous graph transformer to learn the fusion of the history feature from multiple agents and capture the history interaction. Moreover, the goal of prediction is to support future planning. Thus, in the future domain, V2X can provide the prediction results of surrounding objects, and we further extend the graph transformer to capture the future interaction among the ego planning and the other vehicles' intentions and obtain the final future scenario state under a certain planning action. We evaluate the Co‑MTP framework on the real‑world dataset V2X‑Seq, and the results show that Co‑MTP achieves state‑of‑the‑art performance and that both history and future fusion can greatly benefit prediction.

Abstract:
We consider multi‑robot systems under recurring tasks formalized as linear temporal logic (LTL) specifications. To solve the planning problem efficiently, we propose a bottom‑up approach combining offline plan synthesis with online coordination, dynamically adjusting plans via real‑time communication. To address action delays, we introduce a synchronization mechanism ensuring coordinated task execution, leading to a multi‑agent coordination and synchronization framework that is adaptable to a wide range of multi‑robot applications. The software package is developed in Python and ROS2 for broad deployment. We validate our findings through lab experiments involving nine robots showing enhanced adaptability compared to previous methods. Additionally, we conduct simulations with up to ninety agents to demonstrate the reduced computational complexity and the scalability features of our work.

Abstract:
The problem of multi‑robot coverage control has been widely studied to efficiently coordinate a team of robots to cover a desired area of interest. However, this problem faces significant challenges when some robots are lost or deviate from their desired formation during the mission due to faults or cyberattacks. Since a majority of multi‑robot systems (MRSs) rely on communication and relative sensing for their efficient operation, a failure in one robot could result in a cascade of failures in the entire system. In this work, we propose a hierarchical framework for area coverage, combining centralized coordination by leveraging Voronoi partitioning with decentralized reference tracking model predictive control (MPC) for control design. In addition to reference tracking, the decentralized MPC also performs bearing maintenance to enforce a rigid MRS network, thereby enhancing the structural resilience, i.e., the ability to detect and mitigate the effects of localization errors and robot loss during the mission. Furthermore, we show that the resulting control architecture guarantees the recovery of the MRS network in the event of robot loss while maintaining a minimally rigid structure. The effectiveness of the proposed algorithm is validated through numerical simulations.

Abstract:
Efficient task allocation among multiple robots is crucial for optimizing productivity in modern warehouses, particularly in response to the increasing demands of online order fulfillment. This paper addresses the real‑time multi‑robot task allocation (MRTA) problem in dynamic warehouse environments, where tasks emerge with specified start and end locations. The objective is to minimize both the total travel distance of robots and delays in task completion, while also considering practical constraints such as battery management and collision avoidance. We introduce MRTAgent, a dual‑agent Reinforcement Learning (RL) framework inspired by self‑play, designed to optimize task assignments and robot selection to ensure timely task execution. For safe navigation, a modified linear quadratic controller (LQR) approach is employed. To the best of our knowledge, MRTAgent is the first framework to address all critical aspects of practical MRTA problems while supporting continuous robot movements.

Abstract:
Recent advancements in Large Language Models (LLMs) have demonstrated substantial capabilities in enhancing communication and coordination in multi‑robot systems. However, existing methods often struggle to achieve efficient collaboration and decision‑making in dynamic and uncertain environments, which are common in real‑world multi‑robot scenarios. To address these challenges, we propose a novel retrospective actor‑critic framework for multi‑robot collaboration. This framework integrates two key components: (1) an actor that performs real‑time decision‑making based on observations and task directives, and (2) a critic that retrospectively evaluates the outcomes to provide feedback for continuous refinement, such that the proposed framework can adapt effectively to dynamic conditions. Extensive experiments conducted in simulated environments validate the effectiveness of our approach, demonstrating significant improvements in task performance and adaptability. This work offers a robust solution to persistent challenges in robotic collaboration.

Abstract:
Simulation frameworks are essential for the safe development of robotic applications. However, different components of a robotic system are often best simulated in different environments, making full integration challenging. This is particularly true for partially‑open or closed‑source simulators, which commonly suffer from two limitations: (i) lack of runtime control over scene actors via interfaces like ROS, and (ii) restricted access to real‑time state data (e.g., pose, velocity) of scene objects. In the first part of this work, we address these issues by integrating aerial drones simulated in Parrot's Sphinx environment (used for Anafi drones) into the Gazebo simulator. Our approach uses a mirrored drone instance embedded within Gazebo environments to bridge the two simulators. One key application is aerial target tracking, a common task in multi‑robot systems. However, Parrot's default PID‑based controller lacks the agility needed for tracking fast‑moving targets. To overcome this, in the second part of this work we develop a model predictive controller (MPC) that leverages cumulative error states to improve tracking accuracy. Our MPC significantly outperforms the built‑in PID controller in dynamic scenarios, increasing the effectiveness of the overall system. We validate our integrated framework by incorporating the Anafi drone into an existing Gazebo‑based airship simulation and rigorously test the MPC against a custom PID baseline in both simulated and real‑world experiments.

Abstract:
This work addresses the problem of multi‑robot coordination under unknown robot transition models, ensuring that tasks specified by Time Window Temporal Logic are satisfied with user‑defined probability thresholds. We present a bi‑level framework that integrates (i) high‑level task allocation, where tasks are assigned based on the robots' estimated task completion probabilities and expected rewards, and (ii) low‑level distributed policy learning and execution, where robots independently optimize auxiliary rewards while fulfilling their assigned tasks. To handle uncertainty in robot dynamics, our approach leverages real‑time task execution data to iteratively refine expected task completion probabilities and rewards, enabling adaptive task allocation without explicit robot transition models. We theoretically validate the proposed algorithm, demonstrating that the task assignments meet the desired probability thresholds with high confidence. Finally, we demonstrate the effectiveness of our framework through comprehensive simulations.

Abstract:
Multi‑robot systems are essential for environmental monitoring, particularly for tracking spatial phenomena like pollution, soil minerals, and water salinity, and more. This study addresses the challenge of deploying a multi‑robot team for optimal coverage in environments where the density distribution, describing areas of interest, is unknown and changes over time. We propose a fully distributed control strategy that uses Gaussian Processes (GPs) to model the spatial field and balance the trade‑off between learning the field and optimally covering it. Unlike existing approaches, we address a more realistic scenario by handling time‑varying spatial fields, where the exploration‑exploitation trade‑off is dynamically adjusted over time. Each robot operates locally, using only its own collected data and the information shared by the neighboring robots. To address the computational limits of GPs, the algorithm efficiently manages the volume of data by selecting only the most relevant samples for the process estimation. The performance of the proposed algorithm is evaluated through several simulations and experiments, incorporating real‑world data phenomena to validate its effectiveness.

Abstract:
The rapid advancement of Large Language Models (LLMs) has opened new possibilities in Multi‑Robot Systems (MRS), enabling enhanced communication, task allocation and planning, and human‑robot interaction. Unlike traditional single‑robot and multi‑agent systems, MRS poses unique challenges, including coordination, scalability, and real‑world adaptability. This survey provides the first dedicated review of LLM integration into MRS. It systematically categorizes their applications across high‑level task allocation, mid‑level motion planning, low‑level action generation, and human intervention. We highlight key applications in diverse domains, such as household robotics, construction, formation control, target tracking, and robot games, showcasing the versatility and transformative potential of LLMs in MRS. Furthermore, we examine the challenges that limit adapting LLMs to MRS, including mathematical reasoning limitations, hallucination, latency issues, and the need for robust benchmarking systems. Finally, we outline opportunities for future research, emphasizing advancements in fine‑tuning, reasoning techniques, and task‑specific models. This survey aims to guide researchers in the intelligence and real‑world deployment of MRS powered by LLMs. Given the rapidly evolving nature of research in the field, we continuously update the paper list in the open‑source GitHub repository.

Abstract:
Flocking control is essential for multi‑robot systems in diverse applications, yet achieving efficient flocking in congested environments poses challenges regarding computation burdens, performance optimality, and motion safety. This paper addresses these challenges through a multi‑agent reinforcement learning (MARL) framework built on Gibbs Random Fields (GRFs). With GRFs, a multi‑robot system is represented by a set of random variables conforming to a joint probability distribution, thus offering a fresh perspective on flocking reward design. A decentralized training and execution mechanism, which enhances the scalability of MARL concerning robot quantity, is realized using a GRF‑based credit assignment method. An action attention module is introduced to implicitly anticipate the motion intentions of neighboring robots, consequently mitigating potential non‑stationarity issues in MARL. The proposed framework enables learning an efficient distributed control policy for multi‑robot systems in challenging environments with success rate around 99%, as demonstrated through thorough comparisons with state‑of‑the‑art solutions in simulations and experiments. Ablation studies are also performed to validate the efficiency of different framework modules.

Abstract:
A novel onboard tracking approach enabling vision‑based relative localization and communication using Active blinking Marker Tracking (AMT) is introduced in this article. Active blinking markers on multi‑robot team members improve the robustness of relative localization for aerial vehicles in tightly coupled multi‑robot systems during real‑world deployments, while also serving as a resilient communication system. Traditional tracking algorithms struggle with fast‑moving blinking markers due to their intermittent appearance in camera frames and the complexity of associating multiple of these markers across consecutive frames. AMT addresses this by using weighted polynomial regression to predict the future appearance of active blinking markers while accounting for uncertainty in the prediction. In outdoor experiments, the AMT approach outperformed state‑of‑the‑art methods in tracking density, accuracy, and complexity. The experimental validation of this novel tracking approach for relative localization and optical communication involved testing motion patterns motivated by our research on agile multi‑robot deployment.

Abstract:
Many approaches to multi‑robot coordination are susceptible to failure due to communication loss and uncertainty in estimation. We present a real‑time communication‑free distributed navigation algorithm certified by control barrier functions, that models and controls the onboard sensing behavior to keep neighbors in the limited field of view for position estimation. The approach is robust to temporary tracking loss and directly synthesizes control to stabilize visual contact through control Lyapunov‑barrier functions. The main contributions of this paper are a continuous‑time robust trajectory generation and control method certified by control barrier functions for distributed multi‑robot systems and a discrete optimization procedure, namely, MPC‑CBF, to approximate the certified controller. In addition, we propose a linear surrogate of high‑order control barrier function constraints and use sequential quadratic programming to solve MPC‑CBF efficiently.

Abstract:
In this paper, we provide a framework integrating distributed multi‑robot systems and temporal epistemic logic. We show that continuous‑discrete hybrid systems are compatible with logical models of knowledge already used in distributed computing, and demonstrate its usefulness by deriving sufficient epistemic conditions for exploration and gathering robot tasks to be solvable. We provide a separation of the physical and computational aspects of a robotic system, allowing us to decouple the problems related to each and directly use methods from control theory and distributed computing, fields that are traditionally distant in the literature. Finally, we demonstrate a novel approach for reasoning about the knowledge in multi‑robot systems through a principled method of converting a switched hybrid dynamical system into a temporal‑epistemic logic model, passing through an abstract state machine representation. This creates space for methods and results to be exchanged across the fields of control theory, distributed computing and temporal‑epistemic logic, while reasoning about multi‑robot systems.

Abstract:
Anomaly detection is a critical requirement for ensuring safety in autonomous driving. In this work, we leverage Cooperative Perception to share information across nearby vehicles, enabling more accurate identification and consensus of anomalous behaviors in complex traffic scenarios. To account for the real‑world challenge of imperfect communication, we propose a cooperative‑perception‑based anomaly detection framework (CPAD), which is a robust architecture that remains effective under communication interruptions, thereby facilitating reliable performance even in low‑bandwidth settings. Since no multi‑agent anomaly detection dataset exists for vehicle trajectories, we introduce 15,000 different scenarios with a 90,000 trajectories benchmark dataset generated through rule‑based vehicle dynamics analysis. Empirical results demonstrate that our approach outperforms standard anomaly classification methods in F1‑score, AUC and showcase strong robustness to agent connection interruptions.

Abstract:
Collaborative perception significantly enhances individual vehicle perception performance through the exchange of sensory information among agents. However, real‑world deployment faces challenges due to bandwidth constraints and inevitable calibration errors during information exchange. To address these issues, we propose mmCooper, a novel multi‑agent, multi‑stage, communication‑efficient, and collaboration‑robust cooperative perception framework. Our framework leverages a multi‑stage collaboration strategy that dynamically and adaptively balances intermediate‑ and late‑stage information to share among agents, enhancing perceptual performance while maintaining communication efficiency. To support robust collaboration despite potential misalignments and calibration errors, our framework prevents misleading low‑confidence sensing information from transmission and refines the received detection results from collaborators to improve accuracy. The extensive evaluation results on both real‑world and simulated datasets demonstrate the effectiveness of the mmCooper framework and its components.

Abstract:
Control barrier function (CBF)‑based methods provide the minimum modification necessary to formally guarantee safety in the context of quadratic programming, and strict safety guarantee for safety critical systems. However, most CBF‑related derivatives myopically focus on present safety at each time step, a reasoning over a look‑ahead horizon is exactly missing. In this paper, a predictive safety matrix is constructed. We then consolidate the safety condition based on the smallest eigenvalue of the proposed safety matrix. A predefined deconfliction strategy of motion paths is embedded into the trajectory tracking module to manage deadlock conflicts, which computes the deadlock escape velocity with the minimum attitude angle. Comparison results show that the introduction of the predictive term is robust for measurement uncertainty and is immune to oscillations. The proposed deadlock avoidance method avoids a large detour, without obvious stagnation.

Abstract:
Shape formation is one of the most thoroughly studied problems in programmable matter and swarm robotics. However, in many models, the class of shapes that can be formed is highly restricted due to the particles' limited memory. In the hybrid model, an active agent with the computational power of a deterministic finite automaton can form shapes by lifting and placing passive tiles on the triangular lattice. We study the shape reconfiguration problem where the agent additionally has the ability to distinguish so‑called target nodes from non‑target nodes and needs to form a target shape from the initial tile configuration. We present a worst‑case optimal O(mn) algorithm for simply connected target shapes, where m is the initial number of unoccupied target nodes and n is the total number of tiles. Furthermore, we show how an agent can reconfigure a large class of target shapes with holes in O(n^4) steps.

Abstract:
Recent advances have enabled heterogeneous multi‑robot teams to learn complex and effective coordination skills. However, existing neural architectures that support heterogeneous teaming tend to force a trade‑off between expressivity and efficiency. Shared‑parameter designs prioritize sample efficiency by enabling a single network to be shared across all or a pre‑specified subset of robots (via input augmentations), but tend to limit behavioral diversity. In contrast, recent designs employ a separate policy for each robot, enabling greater diversity and expressivity at the cost of efficiency and generalization. Our key insight is that such tradeoffs can be avoided by viewing these design choices as ends of a broad spectrum. Inspired by recent work in transfer and meta learning, and building on prior work in multi‑robot task allocation, we propose Capability‑Aware Shared Hypernetworks (CASH), a soft weight sharing architecture that uses hypernetworks to efficiently learn a flexible shared policy that dynamically adapts to each robot post‑training. By explicitly encoding the impact of robot capabilities (e.g., speed and payload) on collective behavior, CASH enables zero‑shot generalization to unseen robots or team compositions. Our experiments involve multiple heterogeneous tasks, three learning paradigms (imitation learning, value‑based, and policy‑gradient RL), and SOTA multi‑robot simulation (JaxMARL) and hardware (Robotarium) platforms. Across all conditions, we find that CASH generates appropriately‑diverse behaviors and consistently outperforms baseline architectures in terms of performance and sample efficiency during both training and zero‑shot generalization, all with 60%‑80% fewer learnable parameters.

Abstract:
This paper explores an eclectic range of path‑planning methodologies engineered for rolling surfaces. Our focus is on the kinematic intricacies of rolling contact systems, which are investigated through a motion planning lens. Beyond summarizing the approaches to single‑contact rotational surfaces, we explore the challenging domain of spin‑rolling multi‑contact systems. Our work proposes solutions for the higher‑dimensional problem of multiple rotating objects in contact. Venturing beyond kinematics, these methodologies find application across a spectrum of domains, including rolling robots, reconfigurable swarm robotics, micro/nano manipulation, and nonprehensile manipulations. Through meticulously examining established planning strategies, we unveil their practical implementations in various real‑world scenarios, from intricate dexterous manipulation tasks to the nimble manoeuvring of rolling robots and even shape planning of multi‑contact swarms of particles. This study introduces the persistent challenges and unexplored frontiers of robotics, intricately linked to both path planning and mechanism design. As we illuminate existing solutions, we also set the stage for future breakthroughs in this dynamic and rapidly evolving field by highlighting the critical importance of addressing rolling contact problems.

Abstract:
A Multi‑robot system (MRS) provides significant advantages for intricate tasks such as environmental monitoring, underwater inspections, and space missions. However, addressing potential communication failures or the lack of communication infrastructure in these fields remains a challenge. A significant portion of MRS research presumes that the system can maintain communication with proximity constraints, but this approach does not solve situations where communication is either non‑existent, unreliable, or poses a security risk. Some approaches tackle this issue using predictions about other robots while not communicating, but these methods generally only permit agents to utilize first‑order reasoning, which involves reasoning based purely on their own observations. In contrast, to deal with this problem, our proposed framework utilizes Theory of Mind (ToM), employing higher‑order reasoning by shifting a robot's perspective to reason about a belief of others observations. Our approach has two main phases: i) an efficient runtime plan adaptation using active inference to signal intentions and reason about a robot's own belief and the beliefs of others in the system, and ii) a hierarchical epistemic planning framework to iteratively reason about the current MRS mission state. The proposed framework outperforms greedy and first‑order reasoning approaches and is validated using simulations and experiments with heterogeneous robotic systems.

Abstract:
One of the pivotal challenges in a multi‑robot system is how to give attention to accuracy and efficiency while ensuring safety. Prior arts cannot strictly guarantee collision‑free for an arbitrarily large number of robots or the results are considerably conservative. Smoothness of the avoidance trajectory also needs to be further optimized. This paper proposes an accelerationactuated simultaneous obstacle avoidance and trajectory tracking method for arbitrarily large teams of robots, that provides a nonconservative collision avoidance strategy and gives approaches for deadlock avoidance. We propose two ways of deadlock resolution, one involves incorporating an auxiliary velocity vector into the error function of the trajectory tracking module, which is proven to have no influence on global convergence of the tracking error. Furthermore, unlike the traditional methods that they address conflicts after a deadlock occurs, our decision‑making mechanism avoids the near‑zero velocity, which is much more safer and efficient in crowed environments. Extensive comparison show that the proposed method is superior to the existing studies when deployed in a large‑scale robot system, with minimal invasiveness.

Abstract:
In this article we propose a game‑theoretic approach to the multi‑robot task allocation problem using the framework of global games. Each task is associated with a global signal, a real‑valued number that captures the task execution progress and/or urgency. We propose a linear objective function for each robot in the system, which, for each task, increases with global signal and decreases with the number assigned robots. We provide conditions on the objective function hyperparameters to induce a mixed Nash equilibrium, i.e., solutions where all robots are not assigned to a single task. The resulting algorithm only requires the inversion of a matrix to determine a probability distribution over the robot assignments. We demonstrate the performance of our algorithm in simulation and provide direction for applications and future work.

Abstract:
This paper provides an in‑depth review and discussion of the state of the art in redundancy mitigation for the vehicular Collective Perception Service (CPS). We focus on the evolutionary differences between the redundancy mitigation rules proposed in 2019 in ETSI TR 103 562 versus the 2023 technical specification ETSI TS 103 324, which uses a Value of Information (VoI) based mitigation approach. We also critically analyse the academic literature that has sought to quantify the communication challenges posed by the CPS and present a unique taxonomy of the redundancy mitigation approaches proposed using three distinct classifications: object inclusion filtering, data format optimisation, and frequency management. Finally, this paper identifies open research challenges that must be adequately investigated to satisfactorily deploy CPS redundancy mitigation measures. Our critical and comprehensive evaluation serves as a point of reference for those undertaking research in this area.

Abstract:
Large‑Scale Multi‑Agent Systems (LS‑MAS) consist of several autonomous components, interacting in a non‑trivial way, so that the emerging behaviour of the ensemble depends on the individual dynamics of the components and their reciprocal interactions. These models can describe a rich variety of natural systems, as well as artificial ones, characterised by unparalleled scalability, robustness, and flexibility. Indeed, a crucial objective is devising efficient strategies to model and control the spatial behaviours of LS‑MAS to achieve specific goals. However, the inherent complexity of these systems and the wide spectrum of their emerging behaviours pose significant challenges. The overarching goal of this thesis is, therefore, to advance methods for modelling, analyzing and controlling the spatial behaviours of LS‑MAS, with applications to cellular populations and swarm robotics. The thesis begins with an overview of the existing Literature, and is then organized into two distinct parts. In the context of swarm robotics, Part I deals with distributed control algorithms to spatially organize agents on geometric patterns. The contribution is twofold, encompassing both the development of original control algorithms, and providing a novel formal analysis, which allows to guarantee the emergence of specific geometric patterns. In Part II, looking at the spatial behaviours of biological agents, experiments are carried out to study the movement of microorganisms and their response to light stimuli. This allows the derivation and parametrization of mathematical models that capture these behaviours, and pave the way for the development of innovative approaches for the spatial control of microorganisms. The results presented in the thesis were developed by leveraging formal analytical tools, simulations, and experiments, using innovative platforms and original computational frameworks.