Learning the meaning of action commands based on "no news is good news" criterion

Authors:
Kazuaki Tanaka;Xiang Zuo;Yasuaki Sagano;Natsuki Oka
Affiliations:
Kyoto Institute of Technology;Kyoto Institute of Technology;Kyoto Institute of Technology;Kyoto Institute of Technology
Venue:
Proceedings of the 2007 workshop on Multimodal interfaces in semantic interaction
Year:
2007

Citing 3
Cited 1

Technical Note: \cal Q-Learning

Machine Learning
An iterative design methodology for user-friendly natural language office information applications

ACM Transactions on Information Systems (TOIS)
Learning words from sights and sounds: a computational model

Learning words from sights and sounds: a computational model

A robot that learns in stages utilizing scaffolds: toward an active and long-term human-robot interaction

ACE '08 Proceedings of the 2008 International Conference on Advances in Computer Entertainment Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

In the future, robots will become common in our daily life. For using the robot more efficiently, it is desirable that the robot would have learning ability. However, a human teaching process for robot learning in the real environment usually takes a very long period of time. We hence believe that the robot should learn from implicit information which is included in human natural behavior. We direct our attention to the lack of utterance as a kind of implicit information, and insist that the lack of utterance should be interpreted as a positive evaluation of the ongoing action, which we call No News Criterion, in a robot navigation context. In this paper, we propose an efficient command learning algorithm based on the No News Criterion, and demonstrate its effectiveness by a human-robot interaction experiment in the real environment.