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Carnegie Mellon Robotics Institute

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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.

Notes

Associated Center(s) / Consortia: Medical Robotics Technology Center

Associated Lab(s) / Group(s): Robot Learning Lab

Associated Project(s): Personal Robotic Assistants For The Elderly

Number of pages: 8

Text Reference

Joelle Pineau, Geoffrey Gordon, and Sebastian Thrun, "Policy-contingent abstraction for robust robot control," Conference on Uncertainty in Articifical Intelligence (UAI), August, 2003, pp. 477 - 484.

BibTeX Reference

@inproceedings{Pineau_2003_4827,
   author = "Joelle Pineau, Geoffrey Gordon, and Sebastian Thrun",
   title = "Policy-contingent abstraction for robust robot control",
   booktitle = "Conference on Uncertainty in Articifical Intelligence (UAI)",
   pages = "477 - 484",
   month = "August",
   year = "2003",
}

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