Meta-Evolve: Continuous Robot Evolution for One-to-many Policy Transfer

Abstract

We investigate the problem of transferring an expert policy from a source robot to multiple different robots. To solve this problem, we propose a method name Meta-Evolve that uses continuous robot evolution to efficiently transfer the policy to a newly defined meta robot and then to each target robot. Since the meta robot is closer to the target robots, our approach can significantly naive one-to-one policy transfer. We also present three heuristic approaches with theoretical results to determine the meta robot. Experiments have shown that with three target robots, our method is able to improve over the baseline of launching multiple independent one-to-one robot-to-robot policy transfers by up to 2.4$\times$ in terms of training and exploration needed.