Can't You See I Am Bothered? Human-inspired Suggestive Avoidance for Robots

Kanghui Du, Drazen Brscic, Yuyi Liu, Takayuki Kanda

Abstract: We studied how robots could stop people from repeatedly obstructing them by using reactions that people commonly use. From 35 hours of observation of people in a shopping mall, we identified one commonly used reaction, which we named suggestive avoidance. It consists of making a quick movement to the side while rotating the body and gaze toward the obstructing person, in a way that seems to imply that they were bothered by the obstruction. We modeled the human suggestive avoidance behavior, implemented it on a robot, and tested it both in a lab experiment and a field study. The results from the lab study confirmed that people perceive a robot using suggestive avoidance as being more bothered, as well as more human-like. The field study showed that when a robot uses suggestive avoidance people are less likely to bother it again.