Investigating the Effects of Robot Engagement Communication on Learning from Demonstration

Published in International Journal of Social Robotics 2021, 2021

Mingfei Sun, Zhenhui Peng, Meng Xia, Xiaojuan Ma

Abstract: 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 for RLfD. To fill this gap, we first design three types of robot engagement behavior (gaze, 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 has significantly negative influences on the human’s estimation of the simulated robots’ capability and significantly raises their expectation towards the learning outcomes, even though we do not run actual imitation learning algorithms in the experiments. Moreover, imitation behavior affects humans more than gaze 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 improves humans’ perception towards the quality of simulated demonstrations, even if all demonstrations are of the same quality.