A Short Review

Machine Learning Approaches For Motor Learning

Machine learning approaches have seen a considerable number of applications in human movement modeling but remain limited for motor learning. Motor learning requires that motor variability be taken into account and poses new challenges because the algorithms need to be able to differentiate between new movements and variation in known ones. We outline in this project 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.

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Publication

Baptiste Caramiaux, Jules Françoise, Wanyu Liu, Téo Sanchez, and Frédéric Bevilacqua. "Machine Learning Approaches For Motor Learning: A Short Review." Frontiers in Computer Science 2 (2020): 16.