Investigating the Effects of Robot Engagement Communication on Learning from Demonstration
This addresses the problem of improving human-robot interaction in learning scenarios, but it is incremental as it builds on known educational principles without testing actual learning algorithms.
The study investigated whether robot engagement behaviors (attention, imitation, or hybrid) affect human instructors' perceptions and expectations in Robot Learning from Demonstration, finding that engagement communication significantly alters human estimations of robot capability and raises expectations for learning outcomes, with imitation and hybrid behaviors having the strongest effects.
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.