Distributed, predictive perception of actions: a biologically inspired robotics architecture for imitation and learning

ISSN： 1360-0494

Source： Connection Science, Vol.15, Iss.4, 2003-12, pp. : 231-243

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Abstract

One of the most important abilities for an agent's cognitive development in a social environment is the ability to recognize and imitate actions of others. In this paper we describe a cognitive architecture for action recognition and imitation, and present experiments demonstrating its implementation in robots. Inspired by neuroscientific and psychological data, and adopting a 'simulation theory of mind' approach, the architecture uses the motor systems of the imitator in a dual role, both for generating actions, and for understanding actions when performed by others. It consists of a distributed system of inverse and forward models that uses prediction accuracy as a means to classify demonstrated actions. The architecture is also shown to be capable of learning new composite actions from demonstration.