Imitation Learning and Response Facilitation in Embodied Agents

Abstract

Abstract. Humans mimic and imitate others, for example, to learn new actions or even to gain knowledge about the other’s intentions. The basis of this behavior is presumably a selective 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 possibly 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, it connects visual representations of observed actions to graph-based representations of motor commands for hand and arm movement. Forward and inverse models are employed to allow for both fast mimicking responses as well as imitation learning.