Variable-Impedance Muscle Coordination under Slow-Rate Control Frequencies and Limited Observation Conditions Evaluated through Legged Locomotion

TL;DR

This study demonstrates that variable-impedance muscle coordination enhances stability in legged locomotion under limited sensory feedback and slow control frequencies, highlighting the role of morphological computation in motor control.

Contribution

It introduces a hierarchical control framework combining reinforcement learning with a low-level muscle coordination model, revealing how morphological computation reduces control complexity.

Findings

Muscle coordination enables stable locomotion with slow control frequencies.

Limited observation conditions do not impair locomotion stability due to muscle coordination.

Morphological computation offloads high-frequency feedback requirements.

Abstract

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 with variable impedance. However, it remains unclear how such low-level mechanical computation reduces the control 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 muscle coor dination model with mono- and biarticular muscles in monoped 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 muscle coordination contributes to learning process of high-level neural network. The results show that variable-impedance muscle coordination enables stable locomotion even under slow-rate control frequency and limited observation conditions. These findings demonstrate that the morphological computation of muscle coordination effectively offloads high-frequency feedback of the high-level controller and provide a design principle for the controller in motor control.