ontingent abstraction for robust robot control Joelle Pineau, Geoff Gordon and Sebastian Thrun School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 This paper presents a scalable control algorithm that enables a deployed mobile robot to make high-level control decisions under full consideration of its probabilistic belief. We draw on insights from the rich literature of structured robot controllers and hierarchical MDPs to propose PolCA, a hierarchical probabilistic control algorithm which learns both subtask-specific state abstractions and policies. The resulting controller has been successfully implemented onboard a mobile robotic assistant deployed in a nursing facility. To the best of our knowledge, this work is a unique instance of applying POMDPs to highlevel robotic control problems.
Joelle Pineau, Geoffrey J. Gordon, Sebastian Thrun