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This paper introduces a reinforcement learning framework for musculoskeletal locomotion simulation that incorporates muscle synergies extracted from human walking data to constrain the control space. By using a low-dimensional synergy basis as the action space for a muscle-driven 3D model, the framework achieves stable gait across variable speeds, slopes, and uneven terrain. The resulting synergy-constrained controller demonstrates improved biomechanical fidelity, reducing non-physiological knee kinematics and producing ground reaction forces that strongly correlate with human measurements, compared to an unconstrained controller.
Injecting muscle synergy priors into reinforcement learning drastically improves the realism of simulated human locomotion, even with limited real-world data.
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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