用肌肉驱动人体模型实现大规模全身运动学习，训练速度提升数十倍。
Towards Embodied AI with MuscleMimic: Unlocking full-body musculoskeletal motor learning at scale
- 基于物理真实的肌肉驱动人体模型，支持全身与上肢运动模仿。
- 单个通用策略在数日内训练完成，可复现多种人类动作。
- 开源框架降低研究门槛，适合机器人、生物力学与神经控制研究者。
肌肉驱动的人体模型进行运动控制学习受限于生物力学仿真计算成本高，且缺乏经过验证的开放全身体模型。本文提出 MuscleMimic，一个开源的可扩展运动模仿学习框架，支持生理真实、肌肉驱动的人形机器人。该框架提供两个经验证的肌骨骼实体：126块肌肉的固定根部上肢模型（用于双手操作）和416块肌肉的全身体模型（用于行走）。配套的重定向管道可将SMPL格式动捕数据映射到肌骨骼结构，保持运动学与动力学一致性。利用大规模并行GPU仿真，相比以往基于CPU的方法实现数量级训练加速，同时保留完整的碰撞处理能力，使单一通用策略在数天内完成数百种多样化动作的训练。生成策略能忠实复现广泛的人类运动，并可在数小时内微调至新动作。与实验步行与跑步数据对比显示关节运动学相关性均值 r = 0.90，肌肉激活分析揭示仅靠运动学模仿难以完全达到生理真实性。通过降低仿真计算与数据门槛，MuscleMimic推动多样动态动作的系统性模型验证，促进神经肌肉控制研究的广泛参与。代码、模型、检查点及重定向数据集已开源。
原文摘要 · Abstract (English)
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
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