Controlling Social Dynamics with a Parametrized Model of Floor Regulation

Turn-taking is ubiquitous in human communication, yet turn-taking between humans and robots continues to be stilted and awkward for human users. The goal of our work is to build autonomous robot controllers for successfully engaging in human-like turn-taking interactions. Towards this end, we present CADENCE, a novel computational model and architecture that explicitly reasons about the four components of floor regulation: seizing the floor, yielding the floor, holding the floor, and auditing the owner of the floor. The model is parametrized to enable the robot to achieve a range of social dynamics for the human-robot dyad. In a between-groups experiment with 30 participants, our humanoid robot uses this turn-taking system at two contrasting parametrizations to engage users in autonomous object play interactions. Our results from the study show that: (1) manipulating these turn-taking parameters results in significantly different robot behavior; (2) people perceive the robot's behavioral differences and consequently attribute different personalities to the robot; and (3) changing the robot's personality results in different behavior from the human, manipulating the social dynamics of the dyad. We discuss the implications of this work for various contextual applications as well as the key limitations of the system to be addressed in future work.