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Title
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From spreading of behavior to dyadic interaction : a robot learns what to imitate
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Author
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Abstract
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| Imitation learning is a promising way to learn new behavior in robotic multiagent systems and in human-robot interaction. However, imitating agents should be able to decide autonomously which behavior, observed in others, is interesting to copy. This paper shows a method for extraction of meaningful chunks of information from a continuous sequence of observed actions by using a simple recurrent network (Elman Net). Results show that, independently of the high level of task-specific noise, Elman nets can be used for learning through prediction a reoccurring action patterns, observed in another robotic agent. We conclude that this primarily robot to robot interaction study can be generalized to human-robot interaction and show how we use these results for recognizing emotional behaviors in human-robot interaction scenarios. The limitations of the proposed approach and the future directions are discussed. |
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Language
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English
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Source (journal)
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International journal of intelligent systems. - New York
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Publication
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New York
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2011
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ISSN
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0884-8173
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DOI
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10.1002/INT.20464
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Volume/pages
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26
:3
(2011)
, p. 228-245
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ISI
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000287116000004
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Full text (Publisher's DOI)
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