Task and motion planning (TAMP) algorithms aim to help robots achieve task-level goals, while maintaining motion-level feasibility. This paper focuses on TAMP domains that involve robot behaviors that take extended periods of time (e.g., long-distance navigation). In this paper, we develop a visual grounding approach to help robots probabilistically evaluate action feasibility, and introduce a TAMP algorithm, called GROP, that optimizes both feasibility and efficiency. We have collected a dataset that includes 96,000 simulated trials of a robot conducting mobile manipulation tasks, and then used the dataset to learn to ground symbolic spatial relationships for action feasibility evaluation. Compared with competitive TAMP baselines, GROP exhibited a higher task-completion rate while maintaining lower or comparable action costs. In addition to these extensive experiments in simulation, GROP is fully implemented and tested on a real robot system.
@article{arxiv.2202.10667,
title = {Visually Grounded Task and Motion Planning for Mobile Manipulation},
author = {Xiaohan Zhang and Yifeng Zhu and Yan Ding and Yuke Zhu and Peter Stone and Shiqi Zhang},
journal= {arXiv preprint arXiv:2202.10667},
year = {2022}
}
Comments
To be published in IEEE International Conference on Robotics and Automation (ICRA), May 23-27, 2022