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Face-to-face interactions are fundamental to the human social system, and, as such, they are a key ingredient in understanding how processes such as opinion dynamics, news and rumor diffusion, and epidemic spreading may occur. In this paper, we leverage the theoretical understanding provided by mathematical models based on complex networks of face-to-face interaction dynamics to propose a multi-robot system that facilitates experimental investigations of these dynamics in settings where the parameters are controllable. Specifically, we consider a team of Elisa-3 robots and implement a distributed and decentralized control law that enables the key mechanisms of interaction giving rise to face-to-face dynamics. We find that the multi-robot system reproduces the main features of the face-to-face dynamics such as long-tailed power-law distributions of contact durations and time intervals between successive contacts. Remarkably, these features prove to be robust, as they emerge in various experimental settings, as well as under challenging operating conditions of the system.

A Multi-Robot System for the Study of Face-to-Face Interaction Dynamics