Neural learning of embodied interaction dynamics.

Abstract

This paper presents our approach towards realizing a robot which can bootstrap itself towards higher complexity through embodied interaction dynamics with the environment including other agents. First, the elements of interaction dynamics are extracted from conceptual analysis of embodied interaction and its emergence, especially of behavioral imitation. Then three case studies are made, presenting our neural architecture and the robotic experiments on some of the important elements discussed above: self-exploration and entrainment, emergent coordination, and categorizing self-behavior. Finally, we propose that integrating all these elements will be an important step towards realizing the bootstrapping agent envisaged above.