  We describe a shared control methodology that can, without knowledge of the
task, be used to improve a human's control of a dynamic system, be used as a
training mechanism, and be used in conjunction with Imitation Learning to
generate autonomous policies that recreate novel behaviors. Our algorithm
introduces autonomy that assists the human partner by enforcing safety and
stability constraints. The autonomous agent has no a priori knowledge of the
desired task and therefore only adds control information when there is concern
for the safety of the system. We evaluate the efficacy of our approach with a
human subjects study consisting of 20 participants. We find that our shared
control algorithm significantly improves the rate at which users are able to
successfully execute novel behaviors. Experimental results suggest that the
benefits of our safety-aware shared control algorithm also extend to the human
partner's understanding of the system and their control skill. Finally, we
demonstrate how a combination of our safety-aware shared control algorithm and
Imitation Learning can be used to autonomously recreate the demonstrated
behaviors.
