Elephants Don't Pack Groceries: Robot Task Planning for Low Entropy Belief States
This work provides a more efficient task planning method for autonomous robots operating with modern low-entropy perception systems, which is an incremental improvement for robot decision-making under uncertainty.
This paper addresses the challenge of robot task planning in scenarios with low perceptual entropy, proposing an approach that combines belief space representation with classical planning. The method significantly outperforms existing classical and belief space planning approaches in planning time, execution time, and task success rate for grocery packing tasks in simulation.
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.