用1.8小时数据训练，让机器人模仿人类走路时的肌肉活动模式。
KINESIS: Motion Imitation for Human Musculoskeletal Locomotion
- 基于负样本挖掘，无模型学习人体运动控制规律
- 能准确复现人体肌电活动，控制高达290条肌肉
- 适用于文本指令、踢球等复杂下游任务
人类如何移动？强化学习在物理仿真的人形机器人控制上取得显著进展，但传统扭矩控制无法模拟人体运动中的生物力学关节约束及非线性、过驱动的肌腱控制机制。本文提出KINESIS，一种无需模型的运动模仿框架，基于1.8小时的步行数据进行训练，在未见轨迹上实现优异的运动模仿性能。通过负样本挖掘策略，该框架学习到鲁棒的运动先验，并成功应用于文本到控制、目标点到达和足球点球等下游任务。重要的是，KINESIS生成的肌肉激活模式与真实人体肌电（EMG）活动高度相关。结果在不同生物力学模型复杂度下均表现良好，支持对最多290条肌肉的精确控制。其生理合理性使其成为研究人类运动控制难题的有力工具。代码、视频与基准测试已开源。
原文摘要 · Abstract (English)
How do humans move? Advances in reinforcement learning (RL) have produced impressive results in capturing human motion using physics-based humanoid control. However, torque-controlled humanoids fail to model key aspects of human motor control such as biomechanical joint constraints & non-linear and overactuated musculotendon control. We present KINESIS, a model-free motion imitation framework that tackles these challenges. KINESIS is trained on 1.8 hours of locomotion data and achieves strong motion imitation performance on unseen trajectories. Through a negative mining approach, KINESIS learns robust locomotion priors that we leverage to deploy the policy on several downstream tasks such as text-to-control, target point reaching, and football penalty kicks. Importantly, KINESIS learns to generate muscle activity patterns that correlate well with human EMG activity. We show that these results scale seamlessly across biomechanical model complexity, demonstrating control of up to 290 muscles. Overall, the physiological plausibility makes KINESIS a promising model for tackling challenging problems in human motor control. Code, videos and benchmarks are available at https://github.com/amathislab/Kinesis.
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