迈向具身体验AI的MuscleMimic：解锁大规模全身体质运动学习
Towards Embodied AI with MuscleMimic: Unlocking full-body musculoskeletal motor learning at scale
AI总结：
MuscleMimic通过生理真实的人形机器人模型，实现了大规模全身体质运动学习，结合并行GPU模拟，提升了训练效率，验证了运动再现的准确性。
摘要：
学习肌肉驱动的骨骼模型的运动控制受到生物力学精确模拟的计算成本和缺乏经过验证的开放全身体模的限制。本文提出了MuscleMimic，一个开源框架，用于可扩展的运动模仿学习，具有生理真实、肌肉驱动的人形机器人。MuscleMimic提供了两个经过验证的骨骼结构：一个固定根部上半身模型（126块肌肉）用于双臂操作，一个全身模型（416块肌肉）用于运动，以及一个将SMPL格式运动捕捉数据映射到骨骼结构的重定向管道，同时保持运动学和动力学一致性。利用大规模并行GPU模拟，该框架在CPU方法上实现了数量级的训练加速，同时保持全面的碰撞处理，使单个通用策略能够在几天内在数百种不同运动上训练。所得到的策略在完全肌肉控制下忠实再现了广泛的人类运动，并可在数小时内微调到新运动。生物力学验证与实验行走和跑步数据对比显示，关节运动学（均相关性r=0.90）有很强的一致性，而肌肉激活分析揭示了通过运动模仿实现生理真实性的潜力和基本挑战。通过降低骨骼模拟的计算和数据障碍，MuscleMimic使在不同动态运动中的系统模型验证成为可能，并扩大了神经肌肉控制研究的参与度。代码、模型、检查点和重定向数据集可在：https://github.com/amathislab/musclemimic获取。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
