Imitation learning and response facilitation in embodied agents

Authors:
Stefan Kopp;Olaf Graeser
Affiliations:
Artificial Intelligence Group, University of Bielefeld, Bielefeld, Germany;Artificial Intelligence Group, University of Bielefeld, Bielefeld, Germany
Venue:
IVA'06 Proceedings of the 6th international conference on Intelligent Virtual Agents
Year:
2006

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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.