A bayesian reinforcement learning approach for customizing human-robot interfaces

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A bayesian reinforcement learning approach for customizing human-robot interfaces

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International Conference on Intelligent User Interfaces archive
Proceedings of the 13th international conference on Intelligent user interfaces table of contents

Sanibel Island, Florida, USA

SESSION: Short papers table of contents

Pages 355-360

Year of Publication: 2009

ISBN:978-1-60558-168-2

Authors

Amin Atrash	McGill University, Montreal, PQ, Canada
Joelle Pineau	McGill University, Montreal, PQ, Canada

Sponsors

SIGCHI: ACM Special Interest Group on Computer-Human Interaction
ACM: Association for Computing Machinery
SIGART: ACM Special Interest Group on Artificial Intelligence

Publisher

ACM New York, NY, USA

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ABSTRACT

Personal robots are becoming increasingly prevalent, which raises a number of interesting issues regarding the design and customization of interfaces to such platforms. The particular problem addressed by this paper is the use of learning methods to improve the quality and effectiveness of human-machine interaction onboard a robotic wheelchair. In support of this, we present a method for learning and adapting probabilistic models with the aid of a human operator. We use a Bayesian reinforcement learning framework, that allows us to mix learning and execution, as well as take advantage of prior information about the world. We address the problems of learning, handling a partially observable environment, and limiting the number of action requests. We demonstrate empirical feasibility of our approach on an interface for an autonomous wheelchair.

REFERENCES

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