Policy-contingent abstraction for robust robot control
This work addresses robust decision-making for deployed mobile robots in real-world settings, representing an incremental application of existing hierarchical and POMDP methods.
The paper tackles the problem of enabling mobile robots to make high-level decisions under probabilistic belief uncertainty, resulting in a scalable control algorithm successfully deployed in a nursing facility in Pittsburgh, PA.
This paper presents a scalable control algorithm that enables a deployed mobile robot system to make high-level decisions under full consideration of its probabilistic belief. Our approach is based on insights from the rich literature of hierarchical controllers and hierarchical MDPs. The resulting controller has been successfully deployed in a nursing facility near Pittsburgh, PA. To the best of our knowledge, this work is a unique instance of applying POMDPs to high-level robotic control problems.