  Despite the fact that robotic platforms can provide both consistent practice
and objective assessments of users over the course of their training, there are
relatively few instances where physical human robot interaction has been
significantly more effective than unassisted practice or human-mediated
training. This paper describes a hybrid shared control robot, which enhances
task learning through kinesthetic feedback. The assistance assesses user
actions using a task-specific evaluation criterion and selectively accepts or
rejects them at each time instant. Through two human subject studies (total
n=68), we show that this hybrid approach of switching between full transparency
and full rejection of user inputs leads to increased skill acquisition and
short-term retention compared to unassisted practice. Moreover, we show that
the shared control paradigm exhibits features previously shown to promote
successful training. It avoids user passivity by only rejecting user actions
and allowing failure at the task. It improves performance during assistance,
providing meaningful task-specific feedback. It is sensitive to initial skill
of the user and behaves as an `assist-as-needed' control scheme---adapting its
engagement in real time based on the performance and needs of the user. Unlike
other successful algorithms, it does not require explicit modulation of the
level of impedance or error amplification during training and it is permissive
to a range of strategies because of its evaluation criterion. We demonstrate
that the proposed hybrid shared control paradigm with a task-based minimal
intervention criterion significantly enhances task-specific training.
