Can User-Centered Reinforcement Learning Allow a Robot to Attract Passersby without Causing Discomfort?
This addresses the issue of discomfort for passersby in social robot interactions, such as in receptionist or exhibitor roles, though it appears incremental based on related work.
The study tackled the problem of social robots startling passersby by developing a user-centered reinforcement learning method that allows robots to greet and attract attention without causing discomfort, with statistical significance (p<0.01) demonstrated in a field experiment at an office entrance.
The aim of our study was to develop a method by which a social robot can greet passersby and get their attention without causing them to suffer discomfort.A number of customer services have recently come to be provided by social robots rather than people, including, serving as receptionists, guides, and exhibitors. Robot exhibitors, for example, can explain products being promoted by the robot owners. However, a sudden greeting by a robot can startle passersby and cause discomfort to passersby.Social robots should thus adapt their mannerisms to the situation they face regarding passersby.We developed a method for meeting this requirement on the basis of the results of related work. Our proposed method, user-centered reinforcement learning, enables robots to greet passersby and get their attention without causing them to suffer discomfort (p<0.01) .The results of an experiment in the field, an office entrance, demonstrated that our method meets this requirement.