Elephants Don't Pack Groceries: Robot Task Planning for Low Entropy Belief States

TL;DR

This paper introduces a novel robot task planning method that efficiently handles low entropy belief states, improving planning speed and success rates in grocery packing tasks through a combination of belief space and classical planning.

Contribution

The paper presents a new approach that combines belief space representation with classical planning to efficiently solve low entropy goal-directed tasks, outperforming existing methods.

Findings

Outperforms classical and belief space planning in simulation

Reduces planning and execution times

Achieves higher task success rates

Abstract

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 problems 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 an approach for efficient task planning for goal-directed robot reasoning. Our approach 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 our approach with current classical planning and belief space planning approaches by solving low entropy goal-directed grocery packing tasks in simulation. Our approach outperforms these approaches in planning time, execution time, and task success rate in our simulation experiments. We also demonstrate our approach on a real world grocery packing task with physical robot.