Swarm Robotics

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16 papers in the last four weeks, against 1 the four weeks before. 0.2% of all new papers.

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Zero-Shot Scalable Resilience in UAV Swarms: A Decentralized Imitation Learning Framework with Physics-Informed Graph Interactions
Apr 17, 2026Huan Lin, Lianghui DingMulti-Robot Systems Swarm Robotics
Institute of Image Communication and Network Engineering, School of Integrated Circuits, Shanghai Jiao Tong University, Shanghai 200240, China
Flocking phase transition and threat responses in bio-inspired autonomous drone swarms
Dec 24, 2025Matthieu Verdoucq, Dari Trendafilov, Clément Sire +3Aerial Robotics Multi-Robot Systems
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

Apr 17, 2026, cs.LGJ/K move · Enter open · S save

Zero-Shot Scalable Resilience in UAV Swarms: A Decentralized Imitation Learning Framework with Physics-Informed Graph Interactions

Huan Lin, Lianghui Ding

Institute of Image Communication and Network Engineering, School of Integrated Circuits, Shanghai Jiao Tong University, Shanghai 200240, China

Multi-Robot Systems Swarm Robotics Adversarial Imitation Learning Imitation Learning Multi-Robot Coordination Physics-Informed ML+2

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Large-scale Unmanned Aerial Vehicle (UAV) failures can split an unmanned aerial vehicle swarm network into disconnected sub-networks, making decentralized recovery both urgent and difficult. Centralized recovery methods depend on global topology information and become communication-heavy after severe fragmentation. Decentralized heuristics and multi-agent reinforcement learning methods are easier to deploy, but their performance often degrades when the swarm scale and damage severity vary. We present Physics-informed Graph Adversarial Imitation Learning algorithm (PhyGAIL) that adopts centralized training with decentralized execution. PhyGAIL builds bounded local interaction graphs from heterogeneous observations, and uses physics-informed graph neural network to encode directional local interactions as gated message passing with explicit attraction and repulsion. This gives the policy a physically grounded coordination bias while keeping local observations scale-invariant. It also uses scenario-adaptive imitation learning to improve training under fragmented topologies and variable-length recovery episodes. Our analysis establishes bounded local graph amplification, bounded interaction dynamics, and controlled variance of the terminal success signal. A policy trained on 20-UAV swarms transfers directly to swarms of up to 500 UAVs without fine-tuning, and achieves better performance across reconnection reliability, recovery speed, motion safety, and runtime efficiency than representative baselines.

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Latest papers 62

Zero-Shot Scalable Resilience in UAV Swarms: A Decentralized Imitation Learning Framework with Physics-Informed Graph Interactions

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