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A Neural Model for Exploration and Learning of Embodied Movement Patterns

Recently, the study of human motor development mechanisms has attracted the attention of not only brain scientists but also roboticists, who are interested in using these ideas for the development of robot controllers. Unfortunately, the mechanism is still unrevealed. In this paper, we use a synthetic approach to shed light on the development mechanism, to extract the property of the mechanism for a new robot controller. We propose two properties of movement learning in human: (1) compression of the redundant motor command and (2) the mapping from sensor to motor in the coupling among controller, body and environment. To prove our proposition, we construct a neural model with essential features of biological system. In a series of experiments with a simple body model, rhythmic movement (to be learned in early days of infant) is explored and correctly learned; moreover entrainment is observed. These results suggest that our model have the ability to learn rhythmic movement; it is the first step towards understanding of developmental mechanism of human.