Scalable Multi-Robot Cooperation for Multi-Goal Tasks Using Reinforcement Learning

This is the authors' abstract. We don't add key points for this paper.

Coordinated navigation of an arbitrary number of robots to an arbitrary number of goals is a big challenge in robotics, often hindered by scalability limitations of existing strategies. This letter introduces a decentralized multi-agent control system using neural network policies trained in simulation. By leveraging permutation invariant neural network architectures and model-free reinforcement learning, our policy enables robots to prioritize varying numbers of collaborating robots and goals in a zero-shot manner without being biased by ordering or limited by a fixed capacity. We validate the task performance and scalability of our policies through experiments in both simulation and real-world settings. Our approach achieves a 10.3% higher success rate in collaborative navigation tasks compared to a policy without a permutation invariant encoder. Additionally, it finds near-optimal solutions for multi-robot navigation problems while being two orders of magnitude faster than an optimization-based centralized controller. We deploy our multi-goal navigation policies on two wheeled-legged quadrupedal robots, which successfully complete a series of multi-goal navigation missions.