Moderating a Robot's Ability to Influence People Through its Level of Sociocontextual Interactivity

Abstract

A range of situations exist in which it would be useful to influence people's behavior in public spaces, for example to improve the efficiency of passenger flow in congested train stations. We have identified our previously developed Robot Centric paradigm of Human-Robot Interaction (HRI), which positions robots as Interaction Peers, as a potentially suitable model to achieve more effective influence through defining and exploiting the interactivity of robots (that is, their ability to moderate their issued sociocontextual cues based on the behavioral information read from humans). In this paper, we investigate whether increasing a robot's interactivity will increase the effectiveness of its influence on people in public spaces. A two-part study (total n = 273) was conducted in both a major Australian public train station (n = 84 + 105) and a university (n = 84) where passersby encountered a robot, designed with various levels of interactivity, which attempted to influence their passage. The findings suggest that the Robot Centric HRI paradigm generalizes to other robots and application spaces, and enables deliberate moderation of a robot's interactivity, facilitating more nuanced, predictable and systematic influence, and thus yielding greater effectiveness.