Meta-Evolve: 連続的ロボット進化による1対多ポリシー転移
Meta-Evolve: Continuous Robot Evolution for One-to-many Policy Transfer
木構造のロボット進化系列を通じて、1つの熟練ポリシーを複数の異なるロボットへ効率的に転移する手法を提案し、マニピュレーションと歩行タスクで大幅な効率改善を実証した。
著者: Xingyu Liu, Deepak Pathak, Ding Zhao
分類: cs.RO, cs.AI, cs.LG, cs.NE
原文アブストラクト
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 named $Meta$-$Evolve$ that uses continuous robot evolution to efficiently transfer the policy to each target robot through a set of tree-structured evolutionary robot sequences. The robot evolution tree allows the robot evolution paths to be shared, so our approach can significantly outperform naive one-to-one policy transfer. We present a heuristic approach to determine an optimized robot evolution tree. Experiments have shown that our method is able to improve the efficiency of one-to-three transfer of manipulation policy by up to 3.2$\times$ and one-to-six transfer of agile locomotion policy by 2.4$\times$ in terms of simulation cost over the baseline of launching multiple independent one-to-one policy transfers.
