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Imitation Learning and Response Facilitation in Embodied Agents

  • Stefan Kopp
  • Olaf Graeser
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4133)

Abstract

Imitation is supposedly a fundamental mechanism for humans to learn new actions and to gain knowledge about another’s intentions. The basis of this behavior seems to be a direct influencing of the motor system by the perceptual system, affording fast, selective enhancement of a motor response already in the repertoire (response facilitation) as well as learning and delayed reproduction of new actions (true imitation). In this paper, we present an approach to attain these capabilities in virtual embodied agents. Building upon a computational motor control model, our approach connects visual representations of observed hand and arm movements to graph-based representations of motor commands. Forward and inverse models are employed to allow for both fast mimicking responses as well as imitation learning.

Keywords

Forward Model Inverse Model Motor Command Motor Representation Perceptual Representation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Stefan Kopp
    • 1
  • Olaf Graeser
    • 1
  1. 1.Artificial Intelligence GroupUniversity of BielefeldBielefeldGermany

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