Emergence of Motor Synergy in Multi-directional Reaching with Deep Reinforcement Learning

In this study, we apply Deep Reinforcement Learning for handling full-dimensional 7 degrees of freedom arm reaching, and demonstrate the relations among motion error, energy, and synergy emergence during the learning process, to reveal the mechanism of employing motor synergy. Although synergy information has never been encoded into the reward function, the synergy effect naturally emerges, leading to a similar situation as human motion learning. To the best of our knowledge, this is a pioneer study verifying a concurrent relation between the error-energy index and synergy development in DRL for multi-directional reaching tasks. In addition, our proposed feedback-augmented DRL controller shows better capability over DRL only in terms of error-energy index.