Motor synergy and energy efficiency emerge in whole-body locomotion learning.

Humans exploit motor synergies for motor control; however, how they emerge during motor learning is not clearly understood. Few studies have dealt with the computational mechanism for generating synergies. Previously, optimal control generated synergistic motion for the upper limb; however, it has not yet been applied to the high-dimensional whole-body system. We investigated the emergence of synergies through deep reinforcement learning of whole-body locomotion tasks. We carried out a joint-space synergy analysis on whole-body control solutions for walking and running agents in simulated environments. Although a synergy constraint was never encoded into the reward function, the synergy emerged during the learning of walking and running tasks. To investigate the effect of gait symmetry on synergy emergence, we varied the weight level of symmetry loss. Interestingly, increasing the weight of symmetry loss resulted in increased energy efficiency and synergetic motion patterns concurrently. These results illustrate the correlation between motor synergy, energy efficiency, and gait symmetry in whole-body motor learning, reflecting that deep reinforcement learning can generate synergistic gait for highly redundant joint systems, similar to human motor control. This suggests that locomotor synergies can emerge through learning processes, complementing the understanding of synergy emergence mechanisms.