Monte Carlo Value Iteration for Continuous-State POMDPs

Partially observable Markov decision processes (POMDPs) have been successfully applied to various robot motion planning tasks under uncertainty. However, most existing POMDP algorithms assume a discrete state space, while the natural state space of a robot is often continuous. This paper presents Monte Carlo Value Iteration (MCVI) for continuous-state POMDPs. MCVI samples both a robot’s state space and the corresponding belief space, and avoids inefficient a priori discretization of the state space as a grid. Both theoretical results and prelimi- nary experimental results indicate that MCVI is a promising new approach for robot motion planning under uncertainty.

• Type: Conference
• Year: 2010
• Where: WAFR
• Authors: Haoyu Bai, David Hsu, Wee Sun Lee, and Vien A. Ngo
• Keywords: Monte Carlo, Path Planning, POMDP, Robotics