AUTHOR=Caramiaux Baptiste , Françoise Jules , Liu Wanyu , Sanchez Téo , Bevilacqua Frédéric TITLE=Machine Learning Approaches for Motor Learning: A Short Review JOURNAL=Frontiers in Computer Science VOLUME=2 YEAR=2020 URL=https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2020.00016 DOI=10.3389/fcomp.2020.00016 ISSN=2624-9898 ABSTRACT=

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. 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.