  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.
