Online Planning in Continuous POMDPs with Open-Loop Information-Gathering Plans

This paper studies the convergence properties of a receding-horizon information-gathering strategy used in the recently presented RBSR planner for continuous POMDPs. The planner uses a combination of randomized exploration, particle filtering, and goal-seeking heuristic policies to achieve scalability to high-dimensional continuous spaces. We show that convergence is ensured in a subclass of problems where information gain rate exceeds the rate of information loss through process noise. Because these rates are not defined myopically RBSR is able to perform long open-loop information-gathering plans. The technique is demonstrated on a variety of discrete planning benchmarks as well as target-finding and localization problems in up to 7D continuous state spaces.