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 (
) as well as learning and delayed reproduction of new actions (
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.