  We propose a novel criterion for evaluating user input for human-robot
interfaces for known tasks. We use the mode insertion gradient (MIG)---a tool
from hybrid control theory---as a filtering criterion that instantaneously
assesses the impact of user actions on a dynamic system over a time window into
the future. As a result, the filter is permissive to many chosen strategies,
minimally engaging, and skill-sensitive---qualities desired when evaluating
human actions. Through a human study with 28 healthy volunteers, we show that
the criterion exhibits a low, but significant, negative correlation between
skill level, as estimated from task-specific measures in unassisted trials, and
the rate of controller intervention during assistance. Moreover, a MIG-based
filter can be utilized to create a shared control scheme for training or
assistance. In the human study, we observe a substantial training effect when
using a MIG-based filter to perform cart-pendulum inversion, particularly when
comparing improvement via the RMS error measure. Using simulation of a
controlled spring-loaded inverted pendulum (SLIP) as a test case, we observe
that the MIG criterion could be used for assistance to guarantee either task
completion or safety of a joint human-robot system, while maintaining the
system's flexibility with respect to user-chosen strategies.
