  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.
