Learning to Self-Reconfigure for Freeform Modular Robots via Altruism Multi-Agent Reinforcement Learning

Modular robots can change between different configurations to adapt to complex and dynamic environments. Therefore, performing accurate and efficient changes to modular robot system, known as the self-reconfiguration problem, is essential. Existing reconfiguration algorithms are based on discrete motion primitives. However, freeform modular robots are connected without alignment and their motion space is continuous, making existing reconfiguration methods infeasible. In this work, we design a parallel distributed self-reconfiguration algorithm based on multi-agent reinforcement learning for freeform modular robots. We introduce a collaboration mechanism into the reinforcement learning to avoid conflicts in continuous action spaces. Simulations show that our algorithm reduces conflicts and improves effectiveness compared to the baselines.