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Visually Grounded Task and Motion Planning for Mobile Manipulation.
Xiaohan Zhang, Yifeng
Zhu, Yan Ding, Yuke Zhu, Peter
Stone, and Shiqi Zhang.
In International Conference on Robotics
and Automation (ICRA), May 2022.
Project page
Code
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.
@InProceedings{icra2022-zhang,
author={Xiaohan Zhang and Yifeng Zhu and Yan Ding and Yuke Zhu and Peter Stone and Shiqi Zhang},
booktitle={International Conference on Robotics and Automation (ICRA)},
title={Visually Grounded Task and Motion Planning for Mobile Manipulation},
month={May},
year={2022},
volume={},
number={},
doi={},
location={Philadelphia, USA},
abstract={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.},
wwwnote={<a href="https://sites.google.com/view/grop-tamp" target="_blank">Project page</a><br><a href="https://github.com/keke-220/segbot-ur5">Code</a>}
}
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