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.

翻译：人体如何运动？强化学习（RL）的进展在使用基于物理的人形控制捕捉人体运动方面取得了显著成果。然而，基于力矩控制的人形机器人未能模拟人体运动控制的关键方面，例如生物力学关节约束、非线性及过驱动的肌腱控制。我们提出了KINESIS，一个无模型的运动模仿框架，以应对这些挑战。KINESIS使用1.8小时的运动数据进行训练，并在未见过的运动轨迹上实现了强大的运动模仿性能。通过负样本挖掘方法，KINESIS学习到稳健的运动先验，我们将其用于策略部署，以完成多项下游任务，如文本到控制、目标点到达和足球点球。重要的是，KINESIS学习生成与人体肌电图（EMG）活动高度相关的肌肉激活模式。我们表明，这些结果能够无缝地跨生物力学模型复杂度扩展，实现对多至290块肌肉的控制。总体而言，其生理学合理性使KINESIS成为解决人体运动控制中挑战性问题的有前景模型。代码、视频和基准测试参见https://github.com/amathislab/Kinesis。