  Robot Learning from Demonstration (RLfD) is a technique for robots to derive
policies from instructors' examples. Although the reciprocal effects of student
engagement on teacher behavior are widely recognized in the educational
community, it is unclear whether the same phenomenon holds true for RLfD. To
fill this gap, we first design three types of robot engagement behavior
(attention, imitation, and a hybrid of the two) based on the learning
literature. We then conduct, in a simulation environment, a within-subject user
study to investigate the impact of different robot engagement cues on humans
compared to a "without-engagement" condition. Results suggest that engagement
communication significantly changes the human's estimation of the robots'
capability and significantly raises their expectation towards the learning
outcomes, even though we do not run actual learning algorithms in the
experiments. Moreover, imitation behavior affects humans more than attention
does in all metrics, while their combination has the most profound influences
on humans. We also find that communicating engagement via imitation or the
combined behavior significantly improve humans' perception towards the quality
of demonstrations, even if all demonstrations are of the same quality.
