  Designing effective rehabilitation strategies for upper extremities,
particularly hands and fingers, warrants the need for a computational model of
human motor learning. The presence of large degrees of freedom (DoFs) available
in these systems makes it difficult to balance the trade-off between learning
the full dexterity and accomplishing manipulation goals. The motor learning
literature argues that humans use motor synergies to reduce the dimension of
control space. Using the low-dimensional space spanned by these synergies, we
develop a computational model based on the internal model theory of motor
control. We analyze the proposed model in terms of its convergence properties
and fit it to the data collected from human experiments. We compare the
performance of the fitted model to the experimental data and show that it
captures human motor learning behavior well.
