C3F: Constant Collaboration and Communication Framework for Graph-Representation Dynamic Multi-Robotic Systems

Deep reinforcement learning (DRL) methods have been widely applied in distributed multi-robotic systems and successfully realized autonomous learning in many fields. In these fields, robots need to communicate and collaborate with other robots in real time, and reach agreed cognition for task assignment, which puts high requirements on efficiency and stability. However, robots may often get damaged even crash in complex environments, and have to be dynamically substituted. It seems not robust enough for most existing DRL works to make new robots fast adapt to current team policies, causing performance degradation. In this work, we get inspired by the genetic mechanism of social animals' instincts, and propose a robust multi-robotic collaboration and communication framework, C3F . It introduces graph-based representation to discover more features on the relevance among robots, and takes advantage of meta learning mechanism to conclude the general meta policy. When some robots crash and get replaced by new ones, this meta policy will be reused to guide new robots on how to quickly follow the existing collaboration and communication rules, and fast adapt to their roles in the team. The experiments on both the Webots simulator and the Starcraft II platform indicate that our methods have better performance compared with some SOTA methods, showing strong robustness and remarkable adaptability to the dynamic substitution in multi-robotic systems.