Reproduce “Reflex-Informed Neuromuscular Reinforcement Learning for Muscle-Driven Locomotion” on OpenResearch

About this paper

Jian Zhou, Xingyu Zhang, Rui Ma, Yu Cao, Shane Xie, Zhi-qiang Zhang

A Reflex-Informed Neuromuscular Reinforcement Learning framework is presented for muscle-driven locomotion, integrating a fixed reflex controller with an RL policy that modulates four key reflex parameters. This approach generates physiologically plausible and highly adaptive gaits, demonstrating superior kinematic accuracy and dynamic consistency under nominal conditions, adapting to 83% plantarflexor weakness, and recovering from 100 N external pushes without retraining, outperforming direct muscle control RL and offline-optimized reflex methods.

Key insights

• Reinforcement learning can be more effectively utilized by learning to regulate existing, physiologically plausible neuromuscular control mechanisms rather than attempting to discover fundamental muscle coordination from scratch.
• Modulating a compact set of biomechanically meaningful reflex parameters provides an interpretable and efficient learning interface for achieving adaptive neuromuscular control.
• Integrating biological priors (reflexes) into the learning framework significantly improves the physiological plausibility and consistency of muscle-driven locomotion while maintaining high adaptability to changing conditions.

Summary by alphaXiv.