運動学習への機械学習アプローチ：短評
Machine Learning Approaches For Motor Learning: A Short Review
運動学習を対象とした機械学習モデルを概観し、パラメータ適応・転移/メタ学習・強化学習による計画適応の3種類の適応能力を整理したレビュー論文。
著者: Baptiste Caramiaux, Jules Françoise, Wanyu Liu, Téo Sanchez, Frédéric Bevilacqua
分類: cs.LG, cs.HC, cs.RO, stat.ML
原文アブストラクト
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.