Sensorimotor tasks that humans perform are often affected by different sources of uncertainty. Nevertheless, the central nervous system (CNS) can gracefully coordinate our movements. Most learning frameworks rely on the internal model principle, which requires a precise internal representation in the CNS to predict the outcomes of our motor commands. However, learning a perfect internal model in a complex environment over a short period of time is a nontrivial problem. Indeed, achieving proficient motor skills may require years of training for some difficult tasks. Internal models alone may not be adequate to explain the motor adaptation behavior during the early phase of learning. Recent studies investigating the active regulation of motor variability, the presence of suboptimal inference, and model-free learning have challenged some of the traditional viewpoints on the sensorimotor learning mechanism. As a result, it may be necessary to develop a computational framework that can account for these new phenomena. Here, we develop a novel theory of motor learning, based on model-free adaptive optimal control, which can bypass some of the difficulties in existing theories. This new theory is based on our recently developed adaptive dynamic programming (ADP) and robust ADP (RADP) methods and is especially useful for accounting for motor learning behavior when an internal model is inaccurate or unavailable. Our preliminary computational results are in line with experimental observations reported in the literature and can account for some phenomena that are inexplicable using existing models.

中文翻译：

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生物电机控制的无模型鲁棒最优反馈机制

人类执行的感觉运动任务通常受到不同来源的不确定性的影响。尽管如此，中枢神经系统 (CNS) 可以优雅地协调我们的运动。大多数学习框架依赖于内部模型原理，这需要 CNS 中的精确内部表示来预测我们运动命令的结果。然而，在短时间内在复杂的环境中学习一个完美的内部模型是一个不平凡的问题。事实上，要获得熟练的运动技能可能需要多年的训练才能完成一些艰巨的任务。单独的内部模型可能不足以解释学习早期阶段的运动适应行为。最近的研究调查了运动变异性的主动调节、次优推理的存在、和无模型学习已经挑战了一些关于感觉运动学习机制的传统观点。因此，可能有必要开发一个可以解释这些新现象的计算框架。在这里，我们开发了一种新的运动学习理论，基于无模型自适应最优控制，可以绕过现有理论中的一些困难。这一新理论基于我们最近开发的自适应动态规划 (ADP) 和鲁棒 ADP (RADP) 方法，对于解释内部模型不准确或不可用时的运动学习行为特别有用。我们的初步计算结果与文献中报道的实验观察一致，可以解释一些使用现有模型无法解释的现象。