Multi-Robot Coordination

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Multi-robot coordination studies how robots with shared or complementary goals work together in settings such as exploration, transport, warehouses, and aerial-ground missions. Approaches include task allocation and scheduling, joint or distributed motion planning, formation and feedback control, and learned policies that use local observations, communication, or demonstrations. Work increasingly combines these methods with safety constraints, uncertainty-aware replanning, and language or vision models, with evaluation spanning simulation, benchmarks, and physical robot teams. A central challenge is balancing the efficiency and global awareness of centralized planning against the scalability, resilience, and limited communication of decentralized teams, especially when robots or environments differ from those seen in training.

Latest papers

328 papers50 papers in the last four weeks, up 22% on the four weeks before. 0.3% of all new papers.

Mar 9, 2026

Perception-Aware Communication-Free Multi-UAV Coordination in the Wild

We present a communication-free method for safe multi-robot coordination in complex environments such as forests with dense canopy cover, where GNSS is unavailable. Our approach relies on an onboard anisotropic 3D LiDAR sensor used for SLAM as well as for detecting obstacles and neighboring robots. We develop a novel perception-aware 3D navigation framework that enables robots to safely and effectively progress toward a goal region despite limited sensor field-of-view. The approach is evaluated through extensive simulations across diverse scenarios and validated in real-world field experiments, demonstrating its scalability, robustness, and reliability.

Manuel Boldrer, Michal Kamler, Afzal Ahmad +1

Department of Cybernetics, Czech Technical University in Prague, Karlovo namesti 13, 121 35 Prague 2, Czechia.

Multi-Robot Coordination UAV Navigation Safe Robot Navigation Multi-Robot Motion Planning

Mar 9, 2026

DeReCo: Decoupling Representation and Coordination Learning for Object-Adaptive Decentralized Multi-Robot Cooperative Grasping and Lifting

Generalizing decentralized multi-robot cooperative grasping and lifting across objects with diverse shapes and physical properties remains a fundamental challenge. Under decentralized execution, two key challenges arise: object-dependent representation learning under partial observability and coordination learning in multi-agent reinforcement learning (MARL) under non-stationarity. A typical approach jointly optimizes object-dependent representations and coordinated policies in an end-to-end manner while randomizing object shapes and physical properties during training. However, this joint optimization tightly couples representation and coordination learning, which can introduce bidirectional interference: inaccurate representations under partial observability can degrade coordination learning, while non-stationarity in MARL can further degrade representation learning, leading to degraded task performance. Inspired by prior work on decoupling representation and control learning in single-robot settings, we propose DeReCo, a MARL framework that decouples representation and coordination learning for object-adaptive decentralized multi-robot cooperative grasping and lifting. DeReCo adopts a two-phase training strategy: (1) coordination learning with privileged object information, and (2) transition to decentralized execution through reconstruction of object-dependent representations from local observations and retraining of coordination policies using the reconstructed representations. Experimental results show that DeReCo outperforms baselines in simulation on three training objects, generalizes to six unseen objects with varying masses and friction coefficients under similar grasping configurations, and achieves superior performance on two unseen objects in real-robot experiments.

Kazuki Shibata, Ryosuke Sota, Shandil Dhiresh Bosch +3

All the authors are with the Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology (NAIST), Nara, Japan. · Shandil Dhiresh Bosch is with the Department of Cognitive Robotics, Faculty of Mechanical Engineering, Delft University of Technology, Delft, Netherlands.

Multi-Robot Cargo Transportation Multi-Robot Coordination Decentralized MARL Cooperative MARL Data-Efficient Robot Learning

Mar 8, 2026

C $^2$ -Explorer: Contiguity-Driven Task Allocation with Connectivity-Aware Task Representation for Decentralized Multi-UAV Exploration

Efficient multi-UAV exploration under limited communication is severely bottlenecked by inadequate task representation and allocation. Previous task representations either impose heavy communication requirements for coordination or lack the flexibility to handle complex environments, often leading to inefficient traversal. Furthermore, short-horizon allocation strategies neglect spatiotemporal contiguity, causing non-contiguous assignments and frequent cross-region detours. To address this, we propose C

^2

-Explorer, a decentralized framework that constructs a connectivity graph to decompose disconnected unknown components into independent task units. We then introduce a contiguity-driven allocation formulation with a graph-based neighborhood penalty to discourage non-adjacent assignments, promoting more contiguous task sequences over time. Extensive simulation experiments show that C

^2

-Explorer consistently outperforms state-of-the-art (SOTA) baselines, reducing average exploration time by 43.1% and path length by 33.3%. Real-world flights further demonstrate the system's feasibility. The code will be released at https://github.com/Robotics-STAR-Lab/C2-Explorer

Xinlu Yan, Mingjie Zhang, Yuhao Fang +5

School of Intelligence Science and Engineering, Harbin Institute of Technology, Shenzhen, Guangdong, China · Robotics and Autonomous Systems Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China · Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China

Multi-Robot Exploration Multi-Robot Task Allocation Multi-Robot Coordination

Mar 7, 2026

SwiftBot: A Decentralized Platform for LLM-Powered Federated Robotic Task Execution

Federated robotic task execution systems require bridging natural language instructions to distributed robot control while efficiently managing computational resources across heterogeneous edge devices without centralized coordination. Existing approaches face three limitations: rigid hand-coded planners requiring extensive domain engineering, centralized coordination that contradicts federated collaboration as robots scale, and static resource allocation failing to share containers across robots when workloads shift dynamically. We present SwiftBot, a federated task execution platform that integrates LLM-based task decomposition with intelligent container orchestration over a DHT overlay, enabling robots to collaboratively execute tasks without centralized control. SwiftBot achieves 94.3% decomposition accuracy across diverse tasks, reduces task startup latency by 1.5-5.4x and average training latency by 1.4-2.5x, and improves tail latency by 1.2-4.7x under high load through federated warm container migration. Evaluation on multimedia tasks validates that co-designing semantic understanding and federated resource management enables both flexibility and efficiency for robotic task control.

YueMing Zhang, Shuai Xu, Zhengxiong Li +3

California State University, Long Beach, USA · Case Western Reserve University, USA · University of Colorado Denver, USA +2

Multi-Robot Coordination Edge Computing Large Language Model-Based Robot Planning

Mar 7, 2026

ACLM: ADMM-Based Distributed Model Predictive Control for Collaborative Loco-Manipulation

Collaborative transportation of heavy payloads via loco-manipulation is a challenging yet essential capability for legged robots operating in complex, unstructured environments. Centralized planning methods, e.g., holistic trajectory optimization, capture dynamic coupling among robots and payloads but scale poorly with system size, limiting real-time applicability. In contrast, hierarchical and fully decentralized approaches often neglect force and dynamic interactions, leading to conservative behavior. This study proposes an Alternating Direction Method of Multipliers (ADMM)-based distributed model predictive control framework for collaborative loco-manipulation with a team of quadruped robots with manipulators. By exploiting the payload-induced coupling structure, the global optimal control problem is decomposed into parallel individual-robot-level subproblems with consensus constraints. The distributed planner operates in a receding-horizon fashion and achieves fast convergence, requiring only a few ADMM iterations per planning cycle. A wrench-aware whole-body controller executes the planned trajectories, tracking both motion and interaction wrenches. Extensive simulations with up to four robots demonstrate scalability, real-time performance, and robustness to model uncertainty.

Ziyi Zhou, Pengyuan Shu, Ruize Cao +2

The Institute for Robotics and Intelligent Machines, Georgia Institute of Technology.

Model Predictive Control Multi-Robot Coordination

Mar 7, 2026

Energy-Efficient Collaborative Transport of Tether-Suspended Payloads via Rotating Equilibrium

Collaborative aerial transportation of tethered payloads is fundamentally limited by space, power, and weight constraints. Conventional approaches rely on static equilibrium conditions, where each vehicle tilts to generate the forces that ensure they maintain a formation geometry that avoids aerodynamic interactions and collision. This horizontal thrust component represents a significant energy penalty compared to the ideal case in which each vehicle produces purely vertical thrust to lift the payload. Operating in tighter tether configurations can minimize this effect, but at the cost of either having to fly the vehicles in closer proximity, which risks collision, or significantly increasing the length of the tether, which increases complexity and reduces potential use-cases. We propose operating the tether-suspended flying system at a rotating equilibrium. By maintaining steady circular motion, centrifugal forces provide the necessary horizontal tether tension, allowing each quadrotor to generate purely vertical thrust and thus reducing the total force (and power) required compared to an equilibrium where the thrusts are not vertical. It also allows for a wider range of tether configurations to be used without sacrificing efficiency. Results demonstrate that rotating equilibria can reduce power consumption relative to static lifting by up to 20%, making collaborative aerial solutions more practically relevant.

Eric Foss, Andrew Tai, Carlo Bosio +1

High Performance Robotics Laboratory, Department of Mechanical Engineering, University of California Berkeley, CA 94720, United States.

Aerial Robotics Multi-Robot Coordination Quadrotor Control Aerial Manipulation

Mar 6, 2026

Collaborative Planning with Concurrent Synchronization for Operationally Constrained UAV-UGV Teams

Collaborative planning under operational constraints is an essential capability for heterogeneous robot teams tackling complex large-scale real-world tasks. Unmanned Aerial Vehicles (UAVs) offer rapid environmental coverage, but flight time is often limited by energy constraints, whereas Unmanned Ground Vehicles (UGVs) have greater energy capacity to support long-duration missions, but movement is constrained by traversable terrain. Individually, neither can complete tasks such as environmental monitoring. Effective UAV-UGV collaboration therefore requires energy-constrained multi-UAV task planning, traversability-constrained multi-UGV path planning, and crucially, synchronized concurrent co-planning to ensure timely in-mission recharging. To enable these capabilities, we propose Collaborative Planning with Concurrent Synchronization (CoPCS), a learning-based approach that integrates a heterogeneous graph transformer for operationally constrained task encoding with a transformer decoder for joint, synchronized co-planning that enables UAVs and UGVs to act concurrently in a coordinated manner. CoPCS is trained end-to-end under a unified imitation learning paradigm. We conducted extensive experiments to evaluate CoPCS in both robotic simulations and physical robot teams. Experimental results demonstrate that our method provides the novel multi-robot capability of synchronized concurrent co-planning and substantially improves team performance. More details of this work are available on the project website: https://hcrlab.gitlab.io/project/CoPCS.

Zihao Deng, Qianhuang Li, Peng Gao +4

Zihao Deng, Qianhuang Li, Donghyun Kim, and Hao Zhang are with the University of Massachusetts Amherst, Amherst, MA, 01002, USA. · Peng Gao is with North Carolina State University, Raleigh, NC, 27695, USA. · Maggie Wigness and John Rogers are with the U.S. Army DEVCOM Army Research Laboratory (ARL), Adelphi, MD, 20783, USA.

Multi-Robot Coordination Multi-Robot Motion Planning Multi-Robot Task Allocation

Mar 6, 2026

Safe Consensus of Cooperative Manipulation with Hierarchical Event-Triggered Control Barrier Functions

Cooperative transport and manipulation of heavy or bulky payloads by multiple manipulators requires coordinated formation tracking, while simultaneously enforcing strict safety constraints in varying environments with limited communication and real-time computation budgets. This paper presents a distributed control framework that achieves consensus coordination with safety guarantees via hierarchical event-triggered control barrier functions (CBFs). We first develop a consensus-based protocol that relies solely on local neighbor information to enforce both translational and rotational consistency in task space. Building on this coordination layer, we propose a three-level hierarchical event-triggered safety architecture with CBFs, which is integrated with a risk-aware leader selection and smooth switching strategy to reduce online computation. The proposed approach is validated through real-world hardware experiments using two Franka manipulators operating with static obstacles, as well as comprehensive simulations demonstrating scalable multi-arm cooperation with dynamic obstacles. Results demonstrate higher precision cooperation under strict safety constraints, achieving substantially reduced computational cost and communication frequency compared to baseline methods.

Simiao Zhuang, Bingkun Huang, Zewen Yang

Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich (TUM), 80992 Munich, Germany

Control Barrier Functions Multi-Agent Control Multi-Robot Coordination Robotic Manipulation Feedback Control Event-Triggered Control

Feb 25, 2026

ADM-DP: Adaptive Dynamic Modality Diffusion Policy through Vision-Tactile-Graph Fusion for Multi-Agent Manipulation

Multi-agent robotic manipulation remains challenging due to the combined demands of coordination, grasp stability, and collision avoidance in shared workspaces. To address these challenges, we propose the Adaptive Dynamic Modality Diffusion Policy (ADM-DP), a framework that integrates vision, tactile, and graph-based (multi-agent pose) modalities for coordinated control. ADM-DP introduces four key innovations. First, an enhanced visual encoder merges RGB and point-cloud features via Feature-wise Linear Modulation (FiLM) modulation to enrich perception. Second, a tactile-guided grasping strategy uses Force-Sensitive Resistor (FSR) feedback to detect insufficient contact and trigger corrective grasp refinement, improving grasp stability. Third, a graph-based collision encoder leverages shared tool center point (TCP) positions of multiple agents as structured kinematic context to maintain spatial awareness and reduce inter-agent interference. Fourth, an Adaptive Modality Attention Mechanism (AMAM) dynamically re-weights modalities according to task context, enabling flexible fusion. For scalability and modularity, a decoupled training paradigm is employed in which agents learn independent policies while sharing spatial information. This maintains low interdependence between agents while retaining collective awareness. Across seven multi-agent tasks, ADM-DP achieves 12-25% performance gains over state-of-the-art baselines. Ablation studies show the greatest improvements in tasks requiring multiple sensory modalities, validating our adaptive fusion strategy and demonstrating its robustness for diverse manipulation scenarios.

Enyi Wang, Wen Fan, Dandan Zhang

Department of Bioengineering, Imperial-X Initiative, Imperial College London, London, United Kingdom.

Multi-Robot Coordination Diffusion Policy Multimodal Sensor Fusion Robotic Grasping

Feb 24, 2026

Cooperative-Competitive Team Play of Real-World Craft Robots

Multi-agent deep Reinforcement Learning (RL) has made significant progress in developing intelligent game-playing agents in recent years. However, the efficient training of collective robots using multi-agent RL and the transfer of learned policies to real-world applications remain open research questions. In this work, we first develop a comprehensive robotic system, including simulation, distributed learning framework, and physical robot components. We then propose and evaluate reinforcement learning techniques designed for efficient training of cooperative and competitive policies on this platform. To address the challenges of multi-agent sim-to-real transfer, we introduce Out of Distribution State Initialization (OODSI) to mitigate the impact of the sim-to-real gap. In the experiments, OODSI improves the Sim2Real performance by 20%. We demonstrate the effectiveness of our approach through experiments with a multi-robot car competitive game and a cooperative task in real-world settings.

Rui Zhao, Xihui Li, Yizheng Zhang +6

The authors are with Tencent Robotics X Laboratory, Shenzhen, Guangdong, China. · The author is with Tsinghua University, Shenzhen, Guangdong, China.

Cooperative MARL Multi-Robot Coordination Sim-to-Real Transfer Distributed RL

Feb 23, 2026

Rendezvous and Docking of Mobile Ground Robots for Efficient Transportation Systems

In-Motion physical coupling of multiple mobile ground robots has the potential to enable new applications like in-motion transfer that improves efficiency in handling and transferring goods, which tackles current challenges in logistics. A key challenge lies in achieving reliable autonomous in-motion physical coupling of two mobile ground robots starting at any initial position. Existing approaches neglect the modeling of the docking interface and the strategy for approaching it, resulting in uncontrolled collisions that make in-motion physical coupling either impossible or inefficient. To address this challenge, we propose a central mpc approach that explicitly models the dynamics and states of two omnidirectional wheeled robots, incorporates constraints related to their docking interface, and implements an approaching strategy for rendezvous and docking. This novel approach enables omnidirectional wheeled robots with a docking interface to physically couple in motion regardless of their initial position. In addition, it makes in-motion transfer possible, which is 19.75% more time- and 21.04% energy-efficient compared to a non-coupling approach in a logistic scenario.

Lars Fischer, Daniel Flögel, Sören Hohmann

FZI Research Center for Information Technology, Karlsruhe, Germany · Institute of Control Systems, Karlsruhe Institute of Technology, Karlsruhe, Germany

Robotics Multi-Robot Coordination Multi-Robot Motion Planning

Feb 22, 2026

Safe and Interpretable Multimodal Path Planning for Multi-Agent Cooperation

Successful cooperation among decentralized agents requires each agent to quickly adapt its plan to the behavior of other agents. In scenarios where agents cannot confidently predict one another's intentions and plans, language communication can be crucial for ensuring safety. In this work, we focus on path-level cooperation in which agents must adapt their paths to one another in order to avoid collisions or perform physical collaboration such as joint carrying. In particular, we propose a safe and interpretable multimodal path planning method, CaPE (Code as Path Editor), which generates and updates path plans for an agent based on the environment and language communication from other agents. CaPE leverages a vision-language model (VLM) to synthesize a path editing program verified by a model-based planner, grounding communication to path plan updates in a safe and interpretable way. We evaluate our approach in diverse simulated and real-world scenarios, including multi-robot and human-robot cooperation in autonomous driving, household, and joint carrying tasks. Experimental results demonstrate that CaPE can be integrated into different robotic systems as a plug-and-play module, greatly enhancing a robot's ability to align its plan to language communication from other robots or humans. We also show that the combination of the VLM-based path editing program synthesis and model-based planning safety enables robots to achieve open-ended cooperation while maintaining safety and interpretability.

Haojun Shi, Suyu Ye, Katherine M. Guerrerio +5

Johns Hopkins University

Multi-Agent Planning Multi-Robot Coordination Explainable Motion Planning Human-Robot Collaboration Robot Safety Program Synthesis

Feb 19, 2026

Distributed Virtual Model Control for Scalable Human-Robot Collaboration in Shared Workspace

We present a decentralized, agent agnostic, and safety-aware control framework for human-robot collaboration based on Virtual Model Control (VMC). In our approach, both humans and robots are embedded in the same virtual-component-shaped workspace, where motion is the result of the interaction with virtual springs and dampers rather than explicit trajectory planning. A decentralized, force-based stall detector identifies deadlocks, which are resolved through negotiation. This reduces the probability of robots getting stuck in the block placement task from up to 61.2% to zero in our experiments. The framework scales without structural changes thanks to the distributed implementation: in experiments we demonstrate safe collaboration with up to two robots and two humans, and in simulation up to four robots, maintaining inter-agent separation at around 20 cm. Results show that the method shapes robot behavior intuitively by adjusting control parameters and achieves deadlock-free operation across team sizes in all tested scenarios.

Yi Zhang, Omar Faris, Chapa Sirithunge +3

Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom

Human-Robot Collaboration Robot Safety Feedback Control Multi-Robot Coordination

Jan 30, 2026

MOSAIC: Modular Scalable Autonomy for Intelligent Coordination of Heterogeneous Robotic Teams

Mobile robots have become indispensable for exploring hostile environments, such as in space or disaster relief scenarios, but often remain limited to teleoperation by a human operator. This restricts the deployment scale and requires near-continuous low-latency communication between the operator and the robot. We present MOSAIC: a scalable autonomy framework for multi-robot scientific exploration using a unified mission abstraction based on Points of Interest (POIs) and multiple layers of autonomy, enabling supervision by a single operator. The framework dynamically allocates exploration and measurement tasks based on each robot's capabilities, leveraging team-level redundancy and specialization to enable continuous operation. We validated the framework in a space-analog field experiment emulating a lunar prospecting scenario, involving a heterogeneous team of five robots and a single operator. Despite the complete failure of one robot during the mission, the team completed 82.3% of assigned tasks at an Autonomy Ratio of 86%, while the operator workload remained at only 78.2%. These results demonstrate that the proposed framework enables robust, scalable multi-robot scientific exploration with limited operator intervention. We further derive practical lessons learned in robot interoperability, networking architecture, team composition, and operator workload management to inform future multi-robot exploration missions.

David Oberacker, Julia Richter, Philip Arm +10

FZI Forschungszentrum für Informatik, Karlsruhe, Germany · Machine Intelligence and Robotics Lab (MaiRo), Karlsruhe Institute for Technology (KIT), Karlsruhe, Germany · Robotic Systems Lab (RSL), ETH Zürich, Zürich, Switzerland +1

Multi-Robot Systems Multi-Robot Exploration Multi-Robot Coordination Human-Robot Collaboration Multi-Robot Task Allocation

Jan 2, 2026

From Perception to Symbolic Task Planning: Vision-Language Guided Human-Robot Collaborative Structured Assembly

Human-robot collaboration (HRC) in structured assembly requires reliable state estimation and adaptive task planning under noisy perception and human interventions. To address these challenges, we introduce a design-grounded human-aware planning framework for human-robot collaborative structured assembly. The framework comprises two coupled modules. Module I, Perception-to-Symbolic State (PSS), employs vision-language models (VLMs) based agents to align RGB-D observations with design specifications and domain knowledge, synthesizing verifiable symbolic assembly states. It outputs validated installed and uninstalled component sets for online state tracking. Module II, Human-Aware Planning and Replanning (HPR), performs task-level multi-robot assignment and updates the plan only when the observed state deviates from the expected execution outcome. It applies a minimal-change replanning rule to selectively revise task assignments and preserve plan stability even under human interventions. We validate the framework on a 27-component timber-frame assembly. The PSS module achieves 97% state synthesis accuracy, and the HPR module maintains feasible task progression across diverse HRC scenarios. Results indicate that integrating VLM-based perception with knowledge-driven planning improves robustness of state estimation and task planning under dynamic conditions.

Yanyi Chen, Min Deng

Department of Civil, Environmental, and Construction Engineering, Texas Tech University, Lubbock, TX 79409, USA

Human-Robot Collaboration Robotic Assembly Robot Task Planning Symbolic Planning Multi-Robot Coordination

Dec 24, 2025

Flocking phase transition and threat responses in bio-inspired autonomous drone swarms

Collective motion inspired by animal groups offers powerful design principles for autonomous aerial swarms. We present a bio-inspired 3D flocking algorithm in which each drone interacts only with a minimal set of influential neighbors, relying solely on local alignment and attraction cues. By systematically tuning these two interaction gains, we map a phase diagram revealing sharp transitions between swarming and schooling, as well as a critical region where susceptibility, polarization fluctuations, and reorganization capacity peak. Outdoor experiments with a swarm of ten drones, combined with simulations using a calibrated flight-dynamics model, show that operating near this transition enhances responsiveness to external disturbances. When confronted with an intruder, the swarm performs rapid collective turns, transient expansions, and reliably recovers high alignment within seconds. These results demonstrate that minimal local-interaction rules are sufficient to generate multiple collective phases and that simple gain modulation offers an efficient mechanism to adjust stability, flexibility, and resilience in drone swarms.

Matthieu Verdoucq, Dari Trendafilov, Clément Sire +3

Ecole Nationale de l’Aviation Civile, Université de Toulouse, France · Centre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative (CBI), Centre National de la Recherche Scientifique (CNRS) & Université de Toulouse – Paul Sabatier, France · Laboratoire de Physique Théorique, CNRS & Université de Toulouse – Paul Sabatier, France

Aerial Robotics Multi-Robot Systems Swarm Robotics Multi-Agent Control Multi-Agent Collaboration Multi-Robot Coordination

Dec 22, 2025

Collaborative Continuum Robots: A Survey from an Automation-System Perspective

Continuum robots (CRs), owing to their compact structure, inherent compliance, and flexible deformation, have been widely applied in various fields. By coordinating multiple CRs to form collaborative continuum robots (CCRs), task adaptability, flexibility and load capacity can be further improved, thus offering significant advantages. In recent years, interest in this emerging field has grown steadily within the continuum-robotics community. In this survey, a closed-loop task-execution architecture for CCRs is constructed from an automation-system perspective, clarifying the system-level logic that transforms target-task specifications into multi-CR collaborative execution. Based on this architecture, CCRs are classified into three representative collaboration modes, namely separated collaboration, assistance collaboration, and parallel collaboration. Research progress is then systematically summarized and analyzed in terms of key technologies, including multi-CR task allocation, structural design, modeling, motion planning methods, control strategies, and sensing mechanisms. Finally, research opportunities in key technologies and system integration challenges for real-world deployment are discussed, providing guidance for research and application of CCRs.

Xinyu Li, Qian Tang, Guoxin Yin +4

Multi-Robot Coordination Continuum Robot Control Continuum Robot Design

Nov 4, 2025

Census-Based Population Autonomy For Distributed Robotic Teaming

Collaborating teams of robots show promise due to 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.

Tyler M. Paine, Anastasia Bizyaeva, Michael R. Benjamin

Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA · Woods Hole Oceanographic Institution Woods Hole, MA 02543, USA · Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA

Multi-Robot Coordination Distributed Optimization Multi-Robot Task Allocation

Oct 17, 2025

Few-Shot Demonstration-Driven Task Coordination and Trajectory Execution for Multi-Robot Systems

Learning coordinated behaviors for multi-robot systems from only a few demonstrations is difficult because temporal task dependencies and spatial trajectory generation are tightly coupled, which increases the hypothesis space and often yields unstable generalization in data-scarce regimes. We present DDACE, a structured few-shot learning framework that introduces a structural inductive bias by explicitly decoupling temporal coordination from spatial trajectory synthesis. Demonstrations are first processed via spectral clustering to extract coordination structure and form interaction graphs. A Temporal Graph Network predicts action dependencies and sequences, while Gaussian Process models generate progress-parameterized geometric trajectories that adapt to new start/goal configurations. This factorized design reduces hypothesis coupling and improves data efficiency for few-shot multi-robot coordination. Extensive simulation studies and real-robot experiments show that DDACE produces stable coordinated executions from a small number of demonstrations and improves trajectory consistency compared to end-to-end imitation baselines under limited data. Additional materials are available at https://sites.google.com/view/ddace.

Taehyeon Kim, Vishnunandan L. N. Venkatesh, Byung-Cheol Min

Purdue University, West Lafayette, IN, USA · Astemo Americas, Inc., Farmington Hills, MI, USA · Indiana University Bloomington, Bloomington, IN, USA

Robot Motion Generation Imitation Learning Multi-Robot Coordination Robot Imitation Learning

Sep 29, 2025

Prompting Robot Teams with Natural Language

This paper presents a framework to prompt multi-robot teams with high-level tasks using natural language expressions. Our objective is to use the reasoning capabilities of language models in understanding and decomposing multi-robot collaboration and decision-making tasks, but in settings where such models cannot be called at deployment time. However, it is hard to specify the behavior of an individual robot from a team instruction, and have it continuously adapt to actions from other robots. This necessitates a framework with the representational capacity required by the logic and semantics of a task, and yet supports decentralized, real-time operation. We solve this dilemma by recognizing that a task can be represented as a deterministic finite automaton, and that recurrent neural networks (RNNs) can encode numerous automata. This allows us to distill the logic and sequential decompositions of sub-tasks obtained from a language model into an RNN, and align its internal states with the semantics of a given task. This leads to a tiny model that encapsulates the reasoning of the language model and can be implemented onboard. To interpret the internal state of the RNN for a decentralized execution, we train a graph neural network control policy conditioned on the hidden states of the RNN and the language embeddings. We present evaluations on simulated and real-world multi-robot tasks that require sequential and collaborative behavior by the team, demonstrating scalable, robust, real-time performance -- sites.google.com/view/prompting-teams.

Eduardo Sebastián, Nicolas Pfitzer, Ajay Shankar +1

Department of Computer Science and Technology, University of Cambridge, UK · Department of Mechanical and Process Engineering, ETH Zurich, Switzerland

Large Language Model-Based Robot Planning Multi-Robot Coordination Language-Conditioned Robot Control

Sep 22, 2025

Optimization without Future Compromises? Decentralized Coordination via Collective and Reinforcement Learning

Efficient resource allocation in multi-agent systems requires autonomous agents to coordinate their decisions while balancing system-wide objectives with individual costs. This becomes increasingly challenging over long time horizons, where decisions that improve the current allocation may compromise future resource allocation, while decentralized agents have limited observations of the overall system. Multi-agent reinforcement learning (MARL) can learn such long-term dependencies via local observations, but directly applying it to large-scale coordination leads to rapidly growing decision spaces and inefficient training. To this end, we propose Hierarchical Reinforcement and Collective Learning (HRCL), a hierarchical framework that uses MARL to guide, rather than replace, decentralized multi-agent coordination. At the high level, MARL learns strategies that restrict the alternatives considered during coordination and guide agents in balancing system-wide and individual objectives. At the low level, agents perform efficient decentralized coordination under this strategic guidance. This separation reduces the learning space and allows short-term coordination trade-offs to be evaluated according to their long-term effects. Experiments on a synthetic benchmark show that HRCL converges substantially faster than standalone MARL and reduces system-wide and individual costs by 35.53% and 27.05%, respectively. Evaluations on energy self-management and drone swarm sensing further show improved resource allocation, power-peak regulation, and sensing efficiency. These results show that learning strategic guidance for an existing coordination process can retain scalable decentralized coordination without letting short-term decisions compromise future resource allocation.

Chuhao Qin, Evangelos Pournaras

School of Computer Science, University of Leeds, UK

Multi-Agent System Optimization Multi-Agent Coordination Hierarchical RL Multi-Agent Resource Allocation Multi-Robot Coordination Multi-Agent Systems+1

Sep 17, 2025

MIMIC-D: Multi-modal Imitation for MultI-agent Coordination with Decentralized Diffusion Policies

As robots become more integrated in society, their ability to coordinate with other robots and humans on multi-modal tasks (those with multiple valid solutions) is crucial. Such behaviors can be learned from expert demonstrations via imitation learning (IL), but when expert demonstrations are multi-modal, standard IL approaches usually average across modes or collapse to a single mode, preventing effective coordination. Being inspired by diffusion models' ability to capture complex multi-modal trajectory distributions in single-agent settings, we develop a diffusion-based framework for coordinated multi-modal behavior in multi-agent systems. However, existing multi-agent diffusion approaches typically require a centralized planner or explicit communication among agents. This assumption can fail in real-world scenarios where robots must operate independently or with agents like humans that they cannot directly communicate with. Therefore, we propose MIMIC-D, a joint training with decentralized execution paradigm for multi-modal multi-agent IL via diffusion. We jointly train all agents' policies with only local information to achieve implicit coordination. In simulation and hardware experiments, our method exhibits robust multi-modal coordination behavior in various tasks and environments, improving upon state-of-the-art baselines.

Dayi Dong, Maulik Bhatt, Seoyeon Choi +1

Department of Mechanical Engineering, University of California Berkeley, Berkeley, CA 94709, USA

Multi-Agent Coordination Diffusion Policy Imitation Learning Multi-Robot Coordination

Sep 15, 2025

Distributed Event-Triggered Distance-Based Formation Control for Multi-Agent Systems

This paper addresses collaborative formation control for multi-agent systems with limited resources. We consider a team of robots tasked with achieving a desired formation from an arbitrary initial configuration. To reduce unnecessary control updates and conserve resources, we propose a distributed event-triggered formation controller. Unlike the well-studied linear formation controllers, the proposed controller is nonlinear and relies on inter-agent distance measurements. Control updates are triggered only when the measurement error exceeds a state-dependent threshold, ensuring system stability while reducing actuation effort. We also employ a distributed control barrier function to guarantee inter-agent collision avoidance. The proposed controller is validated through simulations and real-world experiments involving different communication topologies, scalability tests, and variations in design parameters, and is also compared to periodic triggering strategies. Results demonstrate that the event-triggered approach significantly reduces control effort while preserving formation performance.

Evangelos Psomiadis, Panagiotis Tsiotras

D. Guggenheim School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, 30332-0150, USA

Multi-Robot Formation Control Multi-Agent Control Multi-Robot Coordination Feedback Control Event-Triggered Control

Sep 12, 2025

Asynchronous Indulgent Gathering of Opaque Robots

We consider the benchmarking problem of gathering in an

(N,f)

-fault system consisting of

N

robots, of which at most

f

may fail at any execution. The {\em traditional} version asks

N-f

non-faulty robots to gather at a point not known a priori. The recently studied {\em indulgent} version asks

N-f

non-faulty robots to gather at the location of a faulty one. Indulgent gathering is impossible in a

(2,1)

-crash system in the

LUMI

model under semi-synchrony, even when infinitely many colors and coordinate axis agreement are available. In this paper, we establish four results for indulgent gathering under asynchrony in

LUMI

, considering a {\em mobility fault} model in which a robot crash only impacts its mobility but not the operation of the light. We first show that indulgent gathering is impossible in a

(2,1)

-mobility system using 2-colored lights, and then give an optimal solution w.r.t. the number of colors using 3-colored lights. We then give two deterministic algorithms for an

(N,f)

-mobility system,

f<N

,

N,f

unknown, that exhibit a nice time-color trade-off: One with time

O(N)

using 7-colored lights and another with time

O(\max\{\ell,f\})

using 26-colored lights, where

\ell< N

is the number of distinct convex layers of robot positions in the initial configuration. Our algorithms for an

(N,f)

-mobility system are the first to be analysed time complexity, can withstand obstructed visibility (opaque robot model) and asynchronous scheduling. Interestingly, our algorithms also solve traditional gathering.

Subhajit Pramanick, Saswata Jana, Partha Sarathi Mandal +1

University of Wroclaw, Poland · Indian Institute of Technology Guwahati, India · Kent State University, USA

Multi-Robot Coordination

Aug 20, 2025

Decentralized Vision-Based Autonomous Aerial Wildlife Monitoring

Wildlife field operations demand efficient parallel deployment methods to identify and interact with specific individuals, enabling simultaneous collective behavioral analysis, and health and safety interventions. Previous robotics solutions approach the problem from the herd perspective, or are manually operated and limited in scale. We propose a decentralized vision-based multi-quadrotor system for wildlife monitoring that is scalable, low-bandwidth, and sensor-minimal (single onboard RGB camera). Our approach enables robust identification and tracking of large species in their natural habitat. We develop novel vision-based coordination and tracking algorithms designed for dynamic, unstructured environments without reliance on centralized communication or control. We validate our system through real-world experiments, demonstrating reliable deployment in diverse field conditions.

Makram Chahine, William Yang, Alaa Maalouf +6

Project CETI, New York 10003, NY, USA · CSAIL MIT, Cambridge MA 02139, USA · SEAS Harvard University, Boston MA 02134, USA

Multi-Robot Coordination Drone-Based Wildlife Monitoring Robotic Perception Wildlife Monitoring Multi-Object Tracking

Jul 31, 2025

Simulation-based planning of Motion Sequences for Automated Procedure Optimization in Multi-Robot Assembly Cells

Reconfigurable multi-robot cells offer a promising approach to meet fluctuating assembly demands. However, the recurrent planning of their configurations introduces new challenges, particularly in generating optimized, coordinated multi-robot motion sequences that minimize the assembly duration. This work presents a simulation-based method for generating such optimized sequences. The approach separates assembly steps into task-related core operations and connecting traverse operations. While core operations are constrained and predetermined, traverse operations offer substantial optimization potential. Scheduling the core operations is formulated as an optimization problem, requiring feasible traverse operations to be integrated using a decomposition-based motion planning strategy. Several solution techniques are explored, including a sampling heuristic, tree-based search and gradient-free optimization. For motion planning, a decomposition method is proposed that identifies specific areas in the schedule, which can be solved independently with modified centralized path planning algorithms. The proposed method generates efficient and collision-free multi-robot assembly procedures that outperform a baseline relying on decentralized, robot-individual motion planning. Its effectiveness is demonstrated through simulation experiments.

Loris Schneider, Marc Ungen, Elias Huber +1

Institute for Material Handling and Logistics (IFL), Karlsruhe Institute of Technology, 76131, Germany · Bosch Corporate Research, Robert Bosch GmbH, 71272 Renningen, Germany

Multi-Robot Motion Planning Multi-Robot Coordination Robot Task Planning

Mar 15, 2025

D4orm: Multi-Robot Trajectories with Dynamics-aware Diffusion Denoised Deformations

This work presents an optimization method for generating kinodynamically feasible and collision-free multi-robot trajectories that exploits an incremental denoising scheme in diffusion models. Our key insight is that high-quality trajectories can be discovered merely by denoising noisy trajectories sampled from a distribution. This approach has no learning component, relying instead on only two ingredients: a dynamical model of the robots to obtain feasible trajectories via rollout, and a fitness function to guide denoising with Monte Carlo gradient approximation. The proposed framework iteratively optimizes a deformation for the previous trajectory with the current denoising process, allows anytime refinement as time permits, supports different dynamics, and benefits from GPU acceleration. Our evaluations for differential-drive and holonomic teams with up to 16 robots in 2D and 3D worlds show its ability to discover high-quality solutions faster than other black-box optimization methods such as MPPI. In a 2D holonomic case with 16 robots, it is almost twice as fast. As evidence for feasibility, we demonstrate zero-shot deployment of the planned trajectories on eight multirotors. Code and video: https://github.com/proroklab/d4orm

Yuhao Zhang, Keisuke Okumura, Heedo Woo +2

University of Cambridge · UK · National Institute of Advanced Industrial Science and Technology (AIST) +1

Multi-Robot Motion Planning Constrained Motion Planning Trajectory Optimization Kinodynamic Motion Planning Trajectory Generation Diffusion-Based Planning+3

Dec 1, 2017

An Optimal Algorithm for Changing from Latitudinal to Longitudinal Formation of Autonomous Aircraft Squadrons

This work presents an algorithm for changing from latitudinal to longitudinal formation of autonomous aircraft squadrons. The maneuvers are defined dynamically by using a predefined set of 3D basic maneuvers. This formation change is necessary when the squadron has to perform tasks which demand both formations, such as lift off, georeferencing, obstacle avoidance and landing. Simulations show that the formation change is done without collision. The time complexity analysis of the transformation algorithm reveals that its efficiency is optimal, and the proof of correctness ensures its longitudinal formation features.

Paulo André Sperandio Giacomin, Elder Moreira Hemerly

Universidade Estadual de Santa Cruz Departamento de Ciências Exatas e Tecnológicas - Área de Informática Ilhéus-BA, Brasil · Instituto Tecnológico de Aeronáutica Departamento de Sistemas e Controle - Divisão de Engenharia Eletrônica São José dos Campos-SP, Brasil

Multi-Robot Formation Control Aerial Robotics Multi-Robot Coordination