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

• URL: http://arxiv.org/abs/2405.03534v1
• Date: Mon, 6 May 2024 14:52:23 GMT
• Title: Meta-Evolve: Continuous Robot Evolution for One-to-many Policy Transfer
• Authors: Xingyu Liu, Deepak Pathak, Ding Zhao,
• Abstract summary: We propose a method that uses continuous robot evolution to efficiently transfer the policy to each target robot. The robot evolution tree allows the robot evolution paths to be shared, so our approach can significantly outperform naive one-to-one policy transfer.
• Score: 68.10957584496866
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 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.

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