Planning under Uncertainty for Reliable Health Care Robotics
We describe a mobile robot system, designed to assist residents of an retirement facility. This system is being developed to respond to an aging population and a predicted shortage of nursing professionals. In this paper, we discuss the task of finding and escorting people from place to place in the facility, a task containing uncertainty throughout the problem.
Planning algorithms that model uncertainty well such as Partially Observable Markov Decision Processes (POMDPs) do not scale tractably to real world problems such as the health care domain. We demonstrate an algorithm for representing real world POMDP problems compactly, which allows us to find good policies in reasonable amounts of time. We show that our algorithm is able to find moving people in close to optimal time, where the optimal policy starts with knowledge of the person’s location.
KeywordsBelief State Neural Information Processing System Linear Manifold Partially Observable Markov Decision Process Dimensionality Reduction Technique
Unable to display preview. Download preview PDF.