Learning and communication via imitation: an autonomous robot perspective

This paper proposes a neural network architecture designed to exhibit learning and communication capabilities via imitation. Our architecture allows a “protoimitation” behavior using the “perception ambiguity” inherent in real environments. In the perspective of turn-taking and gestural communication between two agents, new experiments on movement synchronization in an interaction game are presented. Synchronization is obtained as a global attractor depending on the coupling between agents´ dynamics. We also discuss the unsupervised context of the imitation process and present new experiments in which the same architecture is able to learn perception-action associations without any explicit reinforcement. The learning is based on the ability to detect novelty or irregularities in the communication rhythm