From Perception-Action loops to imitation processes: A bottom-up approach of learning by imitation

Abstract

This paper proposes a neural architecture for a robot in order to learn how to imitate a sequence of movements performed by another robot or by a human. The main idea is that the imitation process does not need to be given to the system but can emerge from a mis-interpretation of the perceived situation at the level of a simple sensory-motor system. The robot controller is based on a PerAc (Perception-Action) architecture. This architecture allows an autonomous robot to learn by itself sensory-motor associations with a delayed reward. Here, we show how the same architecture can also be used by a "student" robot to learn to imitate another robot allowing the student robot to discover by itself solutions to a particular problem or to learn from another robot what to do. We discuss the difficulty linked to the segmentation of the actions to imitate. This imitation problem is demonstrated by a task of learning a sequence of movements and their precise timing. Another interesti...