A reinforcement learning scheme for acquisition of via-point representation of human motion

Humans can generate a complex trajectory by imitative learning of others' movement. A method for learning complex sequential movements, but utilizing a via-point representation is proposed. However, the proposed algorithm for estimating a set of via-points from the complex movement does not involve a learning process such as a learning by trial and error. The algorithm can find the minimum number of via-points, and then specify the unique set of via-points without a trial-and-error process. In this paper, we report an acquisition algorithm for via-point representation through trial and error in a human-like manner. The proposed via-point acquisition algorithm based on reinforcement learning finds a set of via-points that can mimic the reference trajectory by iterative learning using evaluation values of generated movement pattern.