Meta-Evolve: Continuous Robot Evolution for One-to-many Policy Transfer
<|reference_start|>Meta-Evolve: Continuous Robot Evolution for One-to-many Policy Transfer: 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.<|reference_end|>
@article{liu2024meta-evolve:,
    title={Meta-Evolve: Continuous Robot Evolution for One-to-many Policy Transfer},
    author={Xingyu Liu, Deepak Pathak, Ding Zhao},
    journal={arXiv preprint arXiv:2405.03534},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.03534},
    primaryClass={cs.RO cs.AI cs.LG cs.NE}
}