反射感知的神经肌肉强化学习用于肌肉驱动运动
Reflex-Informed Neuromuscular Reinforcement Learning for Muscle-Driven Locomotion
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中文总结 AI 辅助
提出反射感知神经肌肉强化学习框架，利用固定反射控制器与策略生成的残差参数调节关键反射，实现生理合理且鲁棒的肌肉驱动运动。
中文摘要 AI 辅助
肌肉驱动的运动为生成逼真的人体运动提供了一种基于物理的方法。然而，同时实现生理合理性和对肌肉骨骼能力变化及外部干扰的适应性仍然是一个基本挑战。为解决这一局限，我们提出了一种用于肌肉驱动运动的反射感知神经肌肉强化学习框架。在该框架内，一个固定的相位依赖反射控制器作为底层神经肌肉控制机制，而强化学习策略产生四个生物力学上有意义的残差参数，根据当前状态调节与髋部摆动、膝关节支撑和踝关节推进相关的关键反射增益和阈值。实验结果表明，所提出的框架生成了生理上合理的运动，具有改进的运动学精度和动态一致性，以及在名义行走条件下更好的双侧对称性和步态间一致性。学习到的策略在肌肉无力和外部扰动下无需重新训练即可保持鲁棒性。
英文摘要
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.
发表机构
- University of Leeds（利兹大学）
机构由 AI 辅助整理，请以论文原文为准。
