Policy-Contingent Abstraction for Robust Robot Control
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 highlevel robotic control problems.
Cite
Text
Pineau et al. "Policy-Contingent Abstraction for Robust Robot Control." Conference on Uncertainty in Artificial Intelligence, 2003.Markdown
[Pineau et al. "Policy-Contingent Abstraction for Robust Robot Control." Conference on Uncertainty in Artificial Intelligence, 2003.](https://mlanthology.org/uai/2003/pineau2003uai-policy/)BibTeX
@inproceedings{pineau2003uai-policy,
title = {{Policy-Contingent Abstraction for Robust Robot Control}},
author = {Pineau, Joelle and Gordon, Geoffrey J. and Thrun, Sebastian},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2003},
pages = {477-484},
url = {https://mlanthology.org/uai/2003/pineau2003uai-policy/}
}