Reflex informed learning improves muscle driven human walking control
Reflex-Informed Neuromuscular Reinforcement Learning for Muscle-Driven Locomotion
RoboticsGraphicsMachine Learning
Summary
Controlling human-like walking using muscle models is hard because it requires movements to be both realistic and adaptable to changes like muscle weakness or bumps. The authors combine a basic reflex system with reinforcement learning to adjust key muscle-related controls dynamically. This approach helps produce walking patterns that look more natural, stay balanced between legs, and handle disturbances without retraining. The resulting control method mimics how humans might adjust their muscle reflexes when walking under different conditions.
What this means in practice
- •For robotics engineers: Design control systems for legged robots that replicate realistic human muscle-driven walking, improving adaptability to disturbances without retraining.
- •For rehabilitation device developers: Create assistive devices that adjust muscle stimulation dynamically to improve walking stability and symmetry in individuals with muscle weakness.$Commercial implications: This paper enables adaptive neuromuscular control for wearable aids that improve gait quality by modulating reflex gains based on real-time muscle and state feedback.
Authors
Jian Zhou, Xingyu Zhang, Rui Ma, Yu Cao, Shane Xie, Zhi-qiang Zhang
Abstract
Muscle-driven locomotion provides a physically grounded approach to generating realistic human movement. However, achieving both physiological plausibility and adaptability to changes in musculoskeletal capacity and external disturbances remains a fundamental challenge. To address this limitation, we propose a Reflex-Informed Neuromuscular Reinforcement Learning framework for muscle-driven locomotion. Within this framework, a fixed phase-dependent reflex controller serves as the underlying neuromuscular control mechanism, while the reinforcement learning policy produces four biomechanically meaningful residual parameters to modulate key reflex gains and thresholds associated with hip swing, knee support, and ankle propulsion according to the current state. Experimental results demonstrate that the proposed framework generates physiologically plausible locomotion with improved kinematic accuracy and dynamic consistency, as well as better bilateral symmetry and stride-to-stride consistency under nominal walking conditions. The learned policy remains robust under muscle weakness and external perturbations without retraining.