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.

翻译：人类运动产生于高维神经肌肉控制，这使得预测性肌肉骨骼仿真具有挑战性。我们提出了一个生理学启发的强化学习框架，该框架利用肌肉协同约束控制。我们从少量地面行走试验的逆向肌肉骨骼分析中提取了一个低维协同基，并将其用作一个肌肉驱动的三维模型的动作空间，该模型在可变速度、坡度和不平坦地形上进行训练。所得控制器在0.7-1.8 m/s的速度范围和±6°的坡度上生成了稳定的步态，并再现了关节角度、关节力矩和地面反作用力的条件依赖性调制。与无约束控制器相比，协同约束控制减少了非生理性的膝关节运动学，并将膝关节力矩曲线保持在实验包络内。在所有条件下，仿真的垂直地面反作用力与人体测量值高度相关，肌肉激活时序在很大程度上落在受试者间变异范围内。这些结果表明，将神经生理学结构嵌入强化学习中，可以在有限的实验数据下提高预测性人类运动仿真的生物力学保真度和泛化能力。