Policy-contingent abstraction for robust robot control
Conference Paper, Proceedings of 19th Conference on Uncertainty in Artificial Intelligence (UAI '03), pp. 477 - 484, August, 2003
Abstract
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 high-level robotic control problems.
BibTeX
@conference{Pineau-2003-8731,author = {Joelle Pineau and Geoffrey Gordon and Sebastian Thrun},
title = {Policy-contingent abstraction for robust robot control},
booktitle = {Proceedings of 19th Conference on Uncertainty in Artificial Intelligence (UAI '03)},
year = {2003},
month = {August},
pages = {477 - 484},
}
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