A Neural Model for Exploration and Learning of Embodied Movement Patterns

Abstract

Despite increased interest in the study of human motor development among researchers ranging brain scientists to roboticists, many aspects of the mechanisms involved remain to be clarified. Talking a synthetic approach to development, to extract the property of the mechanism for a new robot controller, we propose two movement learning properties in the human being: (1) compression of redundant motor commands and (2) mapping from sensors to motors in the coupling of the controller, the body, and the environment. To prove the feasibility of our proposition, we constructed a neural model having essential biological features. In a series of experiments with a simple body model, rhythmic movement (to be learned in early infancy) is explored and correctly learned; moreover entrainment is observed. Our results suggest that our model can learn rhythmic movement, confirming a first step towards understanding of human development.