Tractable Planning under Uncertainty: Exploiting Structure

T HE problem of planning under uncertainty has received significant attention in the scientific community over the past few years. It is now well-recognized that considering uncertainty during planning and decision-making is imperative to the design of robust computer systems. This is particularly crucial in robotics, where the ability to interact effectively with real-world environments is a prerequisite for success. The Partially Observable Markov Decision Process (POMDP) provides a rich framework for planning under uncertainty. The POMDP model can optimize sequences of actions which are robust to sensor noise, missing information, occlusion, as well as imprecise actuators. While the model is sufficiently rich to address most robotic planning problems, exact solutions are generally intractable for all but the smallest problems. This thesis argues that large POMDP problems can be solved by exploiting natural structural constraints. In support of this, we propose two distinct but complementary algorithms which overcome tractability issues in POMDP planning. PBVI is a sample-based approach which approximates a value function solution by planning over a small number of salient information states. PolCA+ is a hierarchical approach which leverages structural properties of a problem to decompose it into a set of smaller, easy-to-solve, problems. These techniques improve the tractability of POMDP planning to the point where POMDP-based robot controllers are a reality. This is demonstrated through the successful deployment of a nursing assistant robot.