Efficient musculoskeletal simulators and powerful learning algorithms provide computational tools to tackle the grand challenge of understanding biological motor control. Our winning solution for the inaugural NeurIPS MyoChallenge leverages an approach mirroring human skill learning. Using a novel curriculum learning approach, we trained a recurrent neural network to control a realistic model of the human hand with 39 muscles to rotate two Baoding balls in the palm of the hand. In agreement with data from human subjects, the policy uncovers a small number of kinematic synergies, even though it is not explicitly biased toward low-dimensional solutions. However, selectively inactivating parts of the control signal, we found that more dimensions contribute to the task performance than suggested by traditional synergy analysis. Overall, our work illustrates the emerging possibilities at the interface of musculoskeletal physics engines, reinforcement learning, and neuroscience to advance our understanding of biological motor control.

中文翻译：

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通过基于课程的强化学习获得肌肉骨骼技能


高效的肌肉骨骼模拟器和强大的学习算法提供了计算工具，以应对理解生物运动控制的巨大挑战。我们为首届 NeurIPS MyoChallenge 提供的获胜解决方案采用了一种反映人类技能学习的方法。使用一种新颖的课程学习方法，我们训练了一个递归神经网络来控制一个具有 39 块肌肉的人手的真实模型，以旋转手掌中的两个保定球。与来自人类受试者的数据一致，该策略揭示了少量的运动协同作用，尽管它没有明确偏向于低维解决方案。然而，选择性地灭活控制信号的某些部分，我们发现比传统协同分析建议的对任务性能有更多影响的维度。总体而言，我们的工作说明了肌肉骨骼物理引擎、强化学习和神经科学界面的新兴可能性，以推进我们对生物运动控制的理解。