Robots have needs too

Human preferences of distance (proxemics) to a robot significantly impact the performance of the robot's automated speech and gesture recognition during face-to-face, social human-robot interactions. This work investigated how people respond to a sociable robot based on its performance at different locations. We performed an experiment in which the robot's ability to understand social signals was artificially attenuated by distance. Participants (N = 180) instructed the robot using speech and pointing gestures, provided proxemic preferences before and after the interaction, and responded to a questionnaire. Our analysis of questionnaire responses revealed that robot performance factors---rather than human-robot proxemics---are significant predictors of user evaluations of robot competence, anthropomorphism, engagement, likability, and technology adoption. Our behavioral analysis suggests that human proxemic preferences change over time as users interact with and come to understand the needs of the robot, and those changes improve robot performance.