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

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)729-741
Number of pages13
JournalRobotics and Autonomous Systems
Issue number5
Publication statusPublished - 2012 May


  • Cognitive robotics
  • Dynamical system
  • Imitation
  • Neural network

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Mathematics(all)
  • Computer Science Applications


Dive into the research topics of 'Imitating others by composition of primitive actions: A neuro-dynamic model'. Together they form a unique fingerprint.

Cite this