Robotic Motion Learning Framework to Promote Social Engagement

Appl Sci (Basel). 2018 Feb;8(2):241. doi: 10.3390/app8020241. Epub 2018 Feb 5.

Abstract

Imitation is a powerful component of communication between people, and it poses an important implication in improving the quality of interaction in the field of human-robot interaction (HRI). This paper discusses a novel framework designed to improve human-robot interaction through robotic imitation of a participant's gestures. In our experiment, a humanoid robotic agent socializes with and plays games with a participant. For the experimental group, the robot additionally imitates one of the participant's novel gestures during a play session. We hypothesize that the robot's use of imitation will increase the participant's openness towards engaging with the robot. Experimental results from a user study of 12 subjects show that post-imitation, experimental subjects displayed a more positive emotional state, had higher instances of mood contagion towards the robot, and interpreted the robot to have a higher level of autonomy than their control group counterparts did. These results point to an increased participant interest in engagement fueled by personalized imitation during interaction.

Keywords: human-robot interaction; imitation; motion learning; socially assistive robotics.