Visually Grounded Task and Motion Planning for Mobile Manipulation
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.
- Publication:
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arXiv e-prints
- Pub Date:
- February 2022
- DOI:
- 10.48550/arXiv.2202.10667
- arXiv:
- arXiv:2202.10667
- Bibcode:
- 2022arXiv220210667Z
- Keywords:
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- Computer Science - Robotics
- E-Print:
- To be published in IEEE International Conference on Robotics and Automation (ICRA), May 23-27, 2022