Policy-contingent abstraction for robust robot control
Joelle Pineau, Geoffrey Gordon, Sebastian Thrun
TL;DR
This paper introduces a scalable POMDP-based control algorithm that allows mobile robots to make high-level decisions considering their probabilistic beliefs, demonstrated through deployment in a real nursing facility.
Contribution
It presents a novel application of POMDPs to high-level robotic control, combining hierarchical controllers with probabilistic reasoning for real-world deployment.
Findings
Successful deployment in a nursing facility
Scalable control algorithm for high-level decision making
First known application of POMDPs in this context
Abstract
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.
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Taxonomy
TopicsFormal Methods in Verification · Reinforcement Learning in Robotics · Machine Learning and Algorithms