Continuous-State POMDPs with Hybrid Dynamics

摘要

Continuous-state POMDPs provide a natural representation for a variety of tasks, including many in robotics. However, existing 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 (hybrid) dynamics model that can represent multi-modal state-dependent dynamics. We present a new point-based POMDP planning algorithm for solving continuous-state POMDPs using this dynamics model. We also provide a constrained optimization approach for approximating the value function as a mixture of a bounded number of Gaussians. We present results on a set of example problems and demonstrate that when different degrees of state accuracy are needed to accomplish a task, our hybrid continuous-state approach outperforms a standard discrete state technique. 1