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Task and motion planning (TAMP) algorithms aim to help robots achievetask-level goals, while maintaining motion-level feasibility. This paperfocuses on TAMP domains that involve robot behaviors that take extended periodsof time (e.g., long-distance navigation). In this paper, we develop a visualgrounding approach to help robots probabilistically evaluate actionfeasibility, and introduce a TAMP algorithm, called GROP, that optimizes bothfeasibility and efficiency. We have collected a dataset that includes 96,000simulated trials of a robot conducting mobile manipulation tasks, and then usedthe dataset to learn to ground symbolic spatial relationships for actionfeasibility evaluation. Compared with competitive TAMP baselines, GROPexhibited a higher task-completion rate while maintaining lower or comparableaction costs. In addition to these extensive experiments in simulation, GROP isfully implemented and tested on a real robot system.

Visually Grounded Task and Motion Planning for Mobile Manipulation