  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.
