Multi-Robot Pursuit in Parameterized Formation via Imitation Learning
On the map: Offline RL and imitation learning from data → Imitation learning methods
Abstract (arXiv)
This paper studies the problem of multi-robot pursuit of how to coordinate a group of defending robots to capture a faster attacker before it enters a protected area. Such operation for defending robots is challenging due to the unknown avoidance strategy and higher speed of the attacker, coupled with the limited communication capabilities of defenders. To solve this problem, we propose a parameterized formation controller that allows defending robots to adapt their formation shape using five adjustable parameters. Moreover, we develop an imitation-learning based approach integrated with model predictive control to optimize these shape parameters. We make full use of these two techniques to enhance the capture capabilities of defending robots through ongoing training. Both simulation and experiment are provided to verify the effectiveness and robustness of our proposed controller. Simulation results show that defending robots can rapidly learn an effective strategy for capturing the attacker, and moreover the learned strategy remains effective across varying numbers of defenders. Experiment results on real robot platforms further validated these findings.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 9 of its 30 references that are in the map. First posted on arXiv 31 Oct 2024.
Builds on
9 of 30 references.
References · 9
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2015Continuous control with deep reinforcement learningcited by 3,279
- 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
- 2020Decentralized Multi-Agent Pursuit using Deep Reinforcement Learningcited by 21
- 2019Improving Coordination in Small-Scale Multi-Agent Deep Reinforcement Learning through Memory-driven Communicationcited by 18
- 2019Message-Dropout: An Efficient Training Method for Multi-Agent Deep Reinforcement Learningcited by 17
- 2020Cooperative Pursuit with Multi-Pursuer and One Faster Free-moving Evadercited by 8
- 2023Using R-functions to Control the Shape of Soft Robotscited by 2
- 2019Herding an Adversarial Attacker to a Safe Area for Defending Safety-Critical Infrastructurecited by 1
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- 2023Mimicking To Dominate: Imitation Learning Strategies for Success in Multiagent Competitive Games
- 2025MIMIC-D: Multi-modal Imitation for MultI-agent Coordination with Decentralized Diffusion Policies
- 2026GAMBIT: Learning to Plan Continuous Multi-Robot Trajectories
- 2021Bayesian Disturbance Injection: Robust Imitation Learning of Flexible Policies
- 2025Learning to Push, Group, and Grasp: A Diffusion Policy Approach for Multi-Object Delivery
- 2021Learning from Imperfect Demonstrations from Agents with Varying Dynamics
- 2023Multi-Agent Behavior Retrieval: Retrieval-Augmented Policy Training for Cooperative Push Manipulation by Mobile Robots