用肌肉协同模式约束强化学习，让人体运动模拟更真实。
Muscle Synergy Priors Enhance Biomechanical Fidelity in Predictive Musculoskeletal Locomotion Simulation
- 用少量步行数据提取肌肉协同基底，作为强化学习的动作空间。
- 在0.7-1.8 m/s、±6°坡度下生成稳定步态，关节力矩与实验数据吻合。
- 适合运动生物力学、康复工程等需高保真模拟的研究者。
人类运动由高维神经肌肉控制产生，使预测性肌骨骼仿真面临挑战。本文提出一种生理信息引导的强化学习框架，通过肌肉协同作用约束控制策略。从少量地面步行试验的逆肌骨骼分析中提取低维协同基底，并将其作为肌肉驱动三维模型在不同速度、坡度和不平地形上的动作空间。所训练控制器在0.7–1.8 m/s速度范围及±6°坡度条件下生成稳定步态，重现了条件依赖的关节角度、关节力矩和地面反作用力变化。相比无约束控制器，协同约束显著降低非生理性的膝关节运动学，并使膝关节力矩分布保持在实验数据范围内。所有工况下，模拟的垂直地面反作用力与实测数据高度相关，肌肉激活时序大多落在个体间变异范围内。结果表明，将神经生理结构嵌入强化学习可提升预测性人体运动模拟的生物力学保真度与泛化能力，且仅需有限实验数据。
原文摘要 · Abstract (English)
Human locomotion emerges from high-dimensional neuromuscular control, making predictive musculoskeletal simulation challenging. We present a physiology-informed reinforcement-learning framework that constrains control using muscle synergies. We extracted a low-dimensional synergy basis from inverse musculoskeletal analyses of a small set of overground walking trials and used it as the action space for a muscle-driven three-dimensional model trained across variable speeds, slopes and uneven terrain. The resulting controller generated stable gait from 0.7-1.8 m/s and on $\pm$ 6$^{\circ}$ grades and reproduced condition-dependent modulation of joint angles, joint moments and ground reaction forces. Compared with an unconstrained controller, synergy-constrained control reduced non-physiological knee kinematics and kept knee moment profiles within the experimental envelope. Across conditions, simulated vertical ground reaction forces correlated strongly with human measurements, and muscle-activation timing largely fell within inter-subject variability. These results show that embedding neurophysiological structure into reinforcement learning can improve biomechanical fidelity and generalization in predictive human locomotion simulation with limited experimental data.
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