Variable-impedance muscle coordination under slow-rate control frequencies and limited observation conditions evaluated through legged locomotion

Human motor control remains agile and robust despite limited sensory information for feedback, a property attributed to the body's ability to perform morphological computation through muscle coordination (MC) with variable impedance. However, it remains unclear how such low-level mechanical computation reduces the requirements of the high-level controller. In this study, we implement a hierarchical controller consisting of a high-level neural network trained by reinforcement learning and a low-level variable-impedance MC model with mono- and biarticular muscles in legged locomotion task. We systematically restrict the high-level controller by varying the control frequency and by introducing biologically inspired observation conditions: delayed, partial, and substituted observation. Under these conditions, we evaluate how the low-level variable-impedance MC contributes to learning process of high-level neural network. The results show that variable-impedance MC enables stable locomotion even under slow-rate control frequency and limited observation conditions. These findings demonstrate that the morphological computation of MC effectively offloads high-frequency feedback of the high-level controller and provide a design principle for the controller in motor control.