From Perception-Action Loops to Imitation Processes: A Bottom-Up Approach of Learning by Imitation

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 misinterpretation of the perceived situation at the level of a simple sensory-motor system. The robot controller is based on a Perception-Action (PerAc) 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 interesting aspect of this work is that the neural network (NN) used for sequence learning is directly inspired from a brain structure named the hippocampus and mainly involved in memory processes (Banquet et al., 1997). We discuss the importance of imitation processes for the understanding of our high-level cognitive abilities linked to self-recognition and to the recognition of the other as something similar to me.