Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation
Abstract
Learning complex manipulation tasks in realistic, obstructed environments is a challenging problem due to hard exploration in the presence of obstacles and high-dimensional visual observations. Prior work tackles the exploration problem by integrating motion planning and reinforcement learning. However, the motion planner augmented policy requires access to state information, which is often not available in the real-world settings. To this end, we propose to distill a state-based motion planner augmented policy to a visual control policy via (1) visual behavioral cloning to remove the motion planner dependency along with its jittery motion, and (2) vision-based reinforcement learning with the guidance of the smoothed trajectories from the behavioral cloning agent. We evaluate our method on three manipulation tasks in obstructed environments and compare it against various reinforcement learning and imitation learning baselines. The results demonstrate that our framework is highly sample-efficient and outperforms the state-of-the-art algorithms. Moreover, coupled with domain randomization, our policy is capable of zero-shot transfer to unseen environment settings with distractors. Code and videos are available at https://clvrai.com/mopa-pd
Cite
@article{arxiv.2111.06383,
title = {Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation},
author = {I-Chun Arthur Liu and Shagun Uppal and Gaurav S. Sukhatme and Joseph J. Lim and Peter Englert and Youngwoon Lee},
journal= {arXiv preprint arXiv:2111.06383},
year = {2021}
}
Comments
Published at the Conference on Robot Learning (CoRL) 2021