Goal-directed Imitation for Robots: a bio-inspired approach to action understanding and skill learning

Abstract

In this paper we present a robot control architecture for learning by imitation which takes inspiration from recent discoveries in action observation/execution experiments with humans and other primates. The architecture implements two basic processing principles: 1) imitation is primarily directed toward reproducing the goal/end state of an observed action sequence, and 2) the required capacity to understand the motor intention of another agent is based on motor simulation. The control architecture is validated in a robot system imitating in a goal-directed manner a grasping and placing sequence displayed by a human model. During imitation, skill transfer occurs by learning and representing ppropriate goal-directed sequences of motor primitives. After having established computational links between the representations of goal and means, further knowledge about the meaning of objects is transferred (“where to place specific objects”). The robustness of the goal-directed organization of the controller is tested in the presence of incomplete visual information and changes in environmental constraints.