  As robots enter human environments, they will be expected to accomplish a
tremendous range of tasks. It is not feasible for robot designers to
pre-program these behaviors or know them in advance, so one way to address this
is through end-user programming, such as via learning from demonstration (LfD).
While significant work has been done on the mechanics of enabling robot
learning from human teachers, one unexplored aspect is enabling mutual feedback
between both the human teacher and robot during the learning process, i.e.,
implicit learning. In this paper, we explore one aspect of this mutual
understanding, grounding sequences, where both a human and robot provide
non-verbal feedback to signify their mutual understanding during interaction.
We conducted a study where people taught an autonomous humanoid robot a dance,
and performed gesture analysis to measure people's responses to the robot
during correct and incorrect demonstrations.
