Learning motor control for muscle-driven musculoskeletal models is hindered by the computational cost of biomechanically accurate simulation and the scarcity of validated, open full-body models. Here we present MuscleMimic, an open-source framework for scalable motion imitation learning with physiologically realistic, muscle-actuated humanoids. MuscleMimic provides two validated musculoskeletal embodiments - a fixed-root upper-body model (126 muscles) for bimanual manipulation and a full-body model (416 muscles) for locomotion - together with a retargeting pipeline that maps SMPL-format motion capture data onto musculoskeletal structures while preserving kinematic and dynamic consistency. Leveraging massively parallel GPU simulation, the framework achieves order-of-magnitude training speedups over prior CPU-based approaches while maintaining comprehensive collision handling, enabling a single generalist policy to be trained on hundreds of diverse motions within days. The resulting policy faithfully reproduces a broad repertoire of human movements under full muscular control and can be fine-tuned to novel motions within hours. Biomechanical validation against experimental walking and running data demonstrates strong agreement in joint kinematics (mean correlation r = 0.90), while muscle activation analysis reveals both the promise and fundamental challenges of achieving physiological fidelity through kinematic imitation alone. By lowering the computational and data barriers to musculoskeletal simulation, MuscleMimic enables systematic model validation across diverse dynamic movements and broader participation in neuromuscular control research. Code, models, checkpoints, and retargeted datasets are available at: https://github.com/amathislab/musclemimic

翻译：学习肌肉驱动肌肉骨骼模型的运动控制，受限于生物力学精确仿真的计算成本以及经过验证的、开放全身模型的稀缺性。本文提出MuscleMimic，一个用于可扩展运动模仿学习的开源框架，基于生理学逼真、肌肉驱动的人形机器人。MuscleMimic提供了两种经过验证的肌肉骨骼本体——用于双臂操作的固定根部上半身模型（126块肌肉）和用于运动控制的全身模型（416块肌肉）——以及一个重定向管道，该管道在保持运动学和动力学一致性的同时，将SMPL格式的运动捕捉数据映射到肌肉骨骼结构上。利用大规模并行GPU仿真，该框架相对于先前基于CPU的方法实现了数量级的训练加速，同时保持全面的碰撞处理能力，使得单个通用策略能够在数天内于数百种多样化运动上进行训练。所得策略在完全肌肉控制下忠实地再现了广泛的人类运动序列，并可在数小时内微调以适应新动作。针对实验步行和跑步数据的生物力学验证显示，关节运动学具有高度一致性（平均相关系数r=0.90），而肌肉激活分析则揭示了仅通过运动学模仿达到生理保真度的潜力与根本性挑战。通过降低肌肉骨骼仿真的计算与数据门槛，MuscleMimoc使针对多种动态运动的系统模型验证成为可能，并促进了神经肌肉控制研究的更广泛参与。代码、模型、检查点以及重定向数据集均可在以下地址获取：https://github.com/amathislab/musclemimic