Neurorobotic approaches about human motor development

Abstract

Revealing internal mechanisms of motor learning of the human brain is one of a remarkable issue to make intelligent robot systems. Hence, research is being actively conducted in various fields such as neuroscience, robotics, and developmental psychology. According to the Piaget's cognitive development theory, human's abilities including motor skills such as motor babbling and imitating others action develop step by step, and studies of cognitive developmental psychology have been reporting empirical evidence supporting this theory. Since it is known that the human brain has a hierarchical structure in its functional connectivity, brain-like computational models such as hierarchical temporal memory (HTM) have been proposed. In this dissertation, we suggested various brain-like computational models representing human's developmental motor abilities as following Piaget's theory and conducted neurorobotic experiments in terms of cognitive developmental robotics. First, multiple motor behaviors were learned and reproduced on the NAO humanoid robot as suggesting a computational model with a hierarchical self-organizing map (SOM). It successfully implemented on the real-robot and resolved intersection issues with the hierarchical structure. Second, developmental dynamics of a recurrent neural network to learn multiple goal-directed motor behaviors were proposed and examined on the NAO humanoid robot, and a relationship with studies of infant developments was discussed. Third, a deep temporal convolutional neural network (TCNN) for recognizing human action based on body movement with detected pose data was proposed. As examining internal representations of trained model, a relationship with the motor system of the human brain was discussed.