Robots Have Needs Too: People Adapt Their Proxemic Preferences to Improve Autonomous Robot Recognition of Human Social Signals

摘要

An objective of autonomous socially assistive robots is to meet the needs and preferences of human users. However, this can sometimes be at the expense of the robot's own ability to understand social signals produced by the user. In particular, human preferences of distance (proxemics) to the robot can have significant impact on the performance rates of its automated speech and gesture recognition systems. In this work, we investigated how user proxemic preferences changed to improve the robot's understanding human social signals. We performed an experiment in which a robot's ability to understand social signals was artificially varied, either uniformly or attenuated across distance. Participants (N = 100) instructed a robot using speech and pointing gestures, and provided their proxemic preferences before and after the interaction. We report two major findings: 1) people predictably underestimate (based on a Power Law) the distance to the location of robot peak performance; and 2) people adjust their proxemic preferences to be near the perceived location of robot peak performance. This work offers insights into the dynamic nature of human-robot proxemics, and has significant implications for the design of social robots and robust autonomous robot proxemic control systems.