Meta-Evolve: Continuous Robot Evolution for One-to-many Policy Transfer
Xingyu Liu, Deepak Pathak, Ding Zhao
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. Supplementary videos available at the project website: https://sites.google.com/view/meta-evolve.
BibTeX
@inproceedings{
liu2024metaevolve,
title={Meta-Evolve: Continuous Robot Evolution for One-to-many Policy Transfer},
author={Xingyu Liu and Deepak Pathak and Ding Zhao},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=RthOl4jHw5}
}