Robot Task Planning for Low Entropy Belief States [article]

Alphonsus Adu-Bredu and Zhen Zeng and Neha Pusalkar and Odest Chadwicke Jenkins
2020 arXiv   pre-print
Recent advances in computational perception have significantly improved the ability of autonomous robots to perform state estimation with low entropy. Such advances motivate a reconsideration of robot decision-making under uncertainty. Current approaches to solving sequential decision-making problems model states as inhabiting the extremes of the perceptual entropy spectrum. As such, these methods are either incapable of overcoming perceptual errors or asymptotically inefficient in solving
more » ... ems with low perceptual entropy. With low entropy perception in mind, we aim to explore a happier medium that balances computational efficiency with the forms of uncertainty we now observe from modern robot perception. We propose FastDownward Replanner (FD-Replan) as an efficient task planning method for goal-directed robot reasoning. FD-Replan combines belief space representation with the fast, goal-directed features of classical planning to efficiently plan for low entropy goal-directed reasoning tasks. We compare FD-Replan with current classical planning and belief space planning approaches by solving low entropy goal-directed grocery packing tasks in simulation. FD-Replan shows positive results and promise with respect to planning time, execution time, and task success rate in our simulation experiments.
arXiv:2011.09105v1 fatcat:wifm77yvyzha3iriwrmcesdm6m