Abstract
Simultaneous and cooperative muscle activation results in involuntary posture stabilization in vertebrates. However, the mechanism through which more muscles than joints contribute to this stabilization remains unclear. We developed a computational human body model with 949 muscle action lines and 22 joints and examined muscle activation patterns for stabilizing right upper or lower extremity motions at a neutral body posture (NBP) under gravity using actor-critic reinforcement learning (ACRL). Two feedback control models (FCM), muscle length change (FCM-ML) and joint angle differences, were applied to ACRL with a normalized Gaussian network (ACRL-NGN) or deep deterministic policy gradient. Our findings indicate that among the six control methods, ACRL-NGN with FCM-ML, utilizing solely antagonistic feedback control of muscle length change without relying on synergy pattern control or categorizing muscles as flexors, extensors, agonists, or synergists, achieved the most efficient involuntary NBP stabilization. This finding suggests that vertebrate muscles are fundamentally controlled without categorization of muscles for targeted joint motion and are involuntarily controlled to achieve the NBP, which is the most comfortable posture under gravity. Thus, ACRL-NGN with FCM-ML is suitable for controlling humanoid muscles and enables the development of a comfortable seat design.