Recent work has shown that complex manipulation skills, such as pushing or pouring, can be learned through state-of-the-art learning based techniques, such as Reinforcement Learning (RL). However, these methods often have high sample-complexity, are susceptible to domain changes, and produce unsafe motions that a robot should not perform. On the other hand, purely geometric model-based planning can produce complex behaviors that satisfy all the geometric constraints of the robot but might not be dynamically feasible for a given environment. In this work, we leverage a geometric model-based planner to build a mixture of path-policies on which a task-specific meta-policy can be learned to complete the task. In our results, we demonstrate that a successful meta-policy can be learned to push a door, while requiring little data and being robust to model uncertainty of the environment. We tested our method on a 7-DOF Franka-Emika Robot pushing a cabinet door in simulation.
@article{arxiv.2307.04040,
title = {Meta-Policy Learning over Plan Ensembles for Robust Articulated Object Manipulation},
author = {Constantinos Chamzas and Caelan Garrett and Balakumar Sundaralingam and Lydia E. Kavraki and Dieter Fox},
journal= {arXiv preprint arXiv:2307.04040},
year = {2023}
}
Comments
5 pages, Workshop on Learning for Task and Motion Planning (RSS2023)