POMDPs for robotic tasks with mixed observability
SM Ong, Shaowei Png, Daniel Hsu, W. S. Lee · 2009
Partially observable Markov decision processes (POMDPs) provide a principled mathematical framework for motion planning of autonomous robots in uncertain and dynamic environments.They have been successfully applied to various robotic tasks, but a major challenge is to scale up POMDP algorithms for more complex robotic systems.Robotic systems often have mixed observability: even when a robot's state is not fully observable, some components of the state may still be fully observable.Exploiting this, we use a factored model to represent separately the fully and partially observable components of a robot's state and derive a compact lowerdimensional representation of its belief space.We then use this factored representation in conjunction with a point-based algorithm to compute approximate POMDP solutions.Separating fully and partially observable state components using a factored model opens up several opportunities to improve the efficiency of point-based POMDP algorithms.Experiments show that on standard test problems, our new algorithm is many times faster than a leading point-based POMDP algorithm.