Publication
Title
From spreading of behavior to dyadic interaction : a robot learns what to imitate
Author
Abstract
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.
Language
English
Source (journal)
International journal of intelligent systems. - New York
Publication
New York : 2011
ISSN
0884-8173
Volume/pages
26:3(2011), p. 228-245
ISI
000287116000004
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 17.05.2011
Last edited 06.08.2017
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