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

P. Gaussier, S. Moga, J. P. Banquet, M. Quoy

This paper proposes a neural architecture for a robot to learn how to imitate a sequence of movements performed by another robot or by a hu-man. The main idea is that the imitation pro-cess 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 sensori-motor system. We discuss the central position of imitation processes for the understanding of our high level cognitive habilities linked to self-recognition and to the recognition of the other as something similar to me. Another interesting aspect of this work is that the neural network used for sequences learning is directly inspired from a brain structure called the hippocampus and mainly involved in our memorization capabilities.

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