Imitating others by composition of primitive actions: A neuro-dynamic model

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This paper introduces a novel neuro-dynamical model that accounts for possible mechanisms of action imitation and learning. It is considered that imitation learning requires at least two classes of generalization. One is generalization over sensory-motor trajectory variances, and the other class is on cognitive level which concerns on more qualitative understanding of compositional actions by own and others which do not necessarily depend on exact trajectories. This paper describes a possible model dealing with these classes of generalization by focusing on the problem of action compositionality. The model was evaluated in the experiments using a small humanoid robot. The robot was trained with a set of different actions concerning object manipulations which can be decomposed into sequences of action primitives. Then the robot was asked to imitate a novel compositional action demonstrated by a human subject which are composed from prior-learned action primitives. The results showed that the novel action can be successfully imitated by decomposing and composing it with the primitives by means of organizing unified intentional representation hosted by mirror neurons even though the trajectory-level appearance is different between the ones of observed and those of self-generated. (C) 2011 Elsevier B.V. All rights reserved.
Publisher
ELSEVIER SCIENCE BV
Issue Date
2012-05
Language
English
Article Type
Article
Keywords

POSTERIOR PARIETAL CORTEX; SELF-ORGANIZATION; MOTOR; BEHAVIOR; MOVEMENTS; REVIEWS; SYSTEMS

Citation

ROBOTICS AND AUTONOMOUS SYSTEMS, v.60, pp.729 - 741

ISSN
0921-8890
DOI
10.1016/j.robot.2011.11.005
URI
http://hdl.handle.net/10203/174481
Appears in Collection
EE-Journal Papers(저널논문)
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