Imitation Learning from human demonstrations is a promising paradigm to teach robots manipulation skills in the real world, but learning complex long-horizon tasks often requires an unattainable amount of demonstrations. To reduce the high data requirement, we resort to human play data — video sequences of people freely interacting with the environment using their hands. We hypothesize that even with different morphologies, human play data contain rich and salient information about physical interactions that can readily facilitate robot policy learning. Motivated by this, we introduce a hierarchical learning framework named MimicPlay that learns latent plans from human play data to guide low-level visuomotor control trained on a small number of teleoperated demonstrations. With systematic evaluations of 14 long-horizon manipulation tasks in the real world, we show that MimicPlay dramatically outperforms state-of-the-art imitation learning methods in task success rate, generalization ability, and robustness to disturbances.
We introduce MimicPlay, a scalable imitation learning algorithm that exploits the complementary strengths of two data sources: cost-effective human play data and small-scale teleoperated robot demonstration data. Using human play data, the high-level controller learns goal-conditioned latent plans by predicting future 3D human hand trajectories given the goal image. Using robot demonstration data, the low-level controller then generates the robot actions from the latent plans. With this hierarchical design, MimicPlay outperforms prior arts by over 50% in 14 challenging long-horizon manipulation tasks. MimicPlay paves the path for future research to scale up robot imitation learning with affordable human costs.
@article{wang2023mimicplay,
title={Mimicplay: Long-horizon imitation learning by watching human play},
author={Wang, Chen and Fan, Linxi and Sun, Jiankai and Zhang, Ruohan and Fei-Fei, Li and Xu, Danfei and Zhu, Yuke and
Anandkumar, Anima},
journal={arXiv preprint arXiv:2302.12422},
year={2023}
}