Reflex-Informed Neuromuscular Reinforcement Learning for Muscle-Driven Locomotion

2026-09-10

AI Summary

本文提出一种基于反射信息的神经肌肉强化学习框架，通过调节关键反射增益和阈值来改进肌肉驱动运动的生理合理性和适应性。

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.

Problem

• muscle-driven locomotion
• physiological plausibility
• adaptability
• external disturbances
• musculoskeletal capacity

Innovation

• Reflex-Informed Neuromuscular Reinforcement Learning
• muscle-driven locomotion
• phase-dependent reflex controller
• biomechanically meaningful residual parameters