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

Hiroaki Arie*, Takafumi Arakaki, Shigeki Sugano, Jun Tani

*この研究の対応する著者

研究成果: Article査読

22 被引用数 (Scopus)

抄録

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 sensorymotor 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.

本文言語English
ページ(範囲)729-741
ページ数13
ジャーナルRobotics and Autonomous Systems
60
5
DOI
出版ステータスPublished - 2012 5月

ASJC Scopus subject areas

  • 制御およびシステム工学
  • ソフトウェア
  • 数学 (全般)
  • コンピュータ サイエンスの応用

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