Learning Distributed End-to-End Hunting Locomotion for Multiple Quadruped Robots

Abstract

Quadruped robots have demonstrated remarkable versatility in various applications, from search and rescue to exploration. Recent advancements have shifted focus from individual robots to swarms, recognizing the potential of collaborative behaviors to achieve complex tasks beyond the capabilities of a single robot. Inspired by the cooperative hunting behaviors observed in nature, this paper presents a reinforcement learning framework for a swarm of quadruped robots to learn decentralized end-to-end hunting locomotion. In particular, we integrate stable and dynamic locomotion with hunting objectives and utilize a guidance vector as privileged information for efficient training. The framework concerns the control dynamics of quadruped robots, ensuring both low-level stability and high-level hunting coordination in muti-robot environments. The trained policy is deployed onto a real robot system, and the experimental results demonstrate coordinative behavior in various scenarios. The implementation code is released to benefit the community.