Task-Oriented Formation Decision via Reinforcement Learning: Herding an Attacking Swarm

Engineering notes

Engineering notes will be added by the Robot Papers editorial team.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为 VLA、具身智能、人形机器人控制、机器人操作等高价值论文补充中文说明。

Original abstract

Multi-robot systems can accomplish tasks that are difficult for a single robot by organizing into task-specific formations. Different from existing studies on multi-robot shape formation, we here study the task-oriented formation decision problem, with a focus on the herding task. This task is challenging due to the attackers' superior maneuverability and their unknown strategies. To address these challenges, we propose the following novel results. First, we encode the formation shape using a low-dimensional parameter vector. This parametric representation reformulates the formation decision as a parameter optimization problem, thereby resolving the limited flexibility of predefined shapes. By optimizing these formation parameters, the defenders' maneuverability disadvantage is mitigated through a formation shape that continuously adapts to task requirements. Second, we develop a reinforcement learning-based policy to regulate the formation parameters. Trained offline in simulations covering diverse attacking strategies, the learned policy can effectively handle adversarial unpredictability during online deployment. Comparative simulations against three baselines demonstrate that our method can successfully accomplish challenging herding tasks. Additional scalability simulations further verify its applicability to simulated scenarios involving dozens of robots. We also validate the practical feasibility of our method on a physical robotic platform with 3 attackers and 7 defenders.

Links and sources

• Official / arXiv page
• PDF from original source

Comments