Planning in partially-observable switching-mode continuous domains

Authors:
Emma Brunskill;Leslie Pack Kaelbling;Tomás Lozano-Pérez;Nicholas Roy
Affiliations:
Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, USA;Computer Science and AI Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, USA;Computer Science and AI Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, USA;Computer Science and AI Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, USA
Venue:
Annals of Mathematics and Artificial Intelligence
Year:
2010

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Abstract

Continuous-state POMDPs provide a natural representation for a variety of tasks, including many in robotics. However, most existing parametric continuous-state POMDP approaches are limited by their reliance on a single linear model to represent the world dynamics. We introduce a new switching-state dynamics model that can represent multi-modal state-dependent dynamics. We present the Switching Mode POMDP (SM-POMDP) planning algorithm for solving continuous-state POMDPs using this dynamics model. We also consider several procedures to approximate the value function as a mixture of a bounded number of Gaussians. Unlike the majority of prior work on approximate continuous-state POMDP planners, we provide a formal analysis of our SM-POMDP algorithm, providing bounds, where possible, on the quality of the resulting solution. We also analyze the computational complexity of SM-POMDP. Empirical results on an unmanned aerial vehicle collisions avoidance simulation, and a robot navigation simulation where the robot has faulty actuators, demonstrate the benefit of SM-POMDP over a prior parametric approach.