论文标题
机器学习方法的运动学习方法：简短的评论
Machine Learning Approaches For Motor Learning: A Short Review
论文作者
论文摘要
机器学习方法在人类运动建模中有相当大的应用，但仍有限制运动学习。运动学习需要计算电动机变异性，并提出新的挑战，因为算法需要能够区分新运动和已知变化的变化。在此简短的评论中，我们概述了现有的机器学习模型，用于运动学习及其适应能力。我们识别并描述了三种类型的适应性：概率模型中的参数适应性，深层神经网络中的转移和元学习以及通过增强学习计划适应。总而言之，我们讨论了将这些模型应用于运动学习支持系统领域的挑战。
Machine learning approaches have seen considerable applications in human movement modeling, but remain limited for motor learning. Motor learning requires accounting for motor variability, and poses new challenges as the algorithms need to be able to differentiate between new movements and variation of known ones. In this short review, we outline existing machine learning models for motor learning and their adaptation capabilities. We identify and describe three types of adaptation: Parameter adaptation in probabilistic models, Transfer and meta-learning in deep neural networks, and Planning adaptation by reinforcement learning. To conclude, we discuss challenges for applying these models in the domain of motor learning support systems.
