Integrated perception and planning in the continuous space: A POMDP approach

Abstract

The partially observable Markov decision process (POMDP) provides a principled mathematical model for integrating perception and planning, a major challenge in robotics. While there are efficient algorithms for moderately large discrete POMDPs, continuous models are often more natural for robotic tasks, and currently there are no practical algorithms that handle continuous POMDPs at an interesting scale. This paper presents an algorithm for continuous-state, continuous-observation POMDPs. We provide experimental results demonstrating its potential in robot planning and learning under uncertainty and a theoretical analysis of its performance. A direct benefit of the algorithm is to simplify model construction.