A key skill for social robots in the wild will be to understand the structure and dynamics of conversational groups in order to fluidly participate in them. Social scientists have long studied the rich complexity underlying such focused encounters, or F-formations. However, current state-of-the-art algorithms that robots might use to recognize F-formations are highly heuristic and quite brittle. In this report, we explore a data-driven approach to detect F-formations from sets of tracked human positions and orientations, trained and evaluated on two openly available human-only datasets and a small human-robot dataset that we collected. We also discuss the potential for further computational characterization of F-formations beyond simply detecting their occurrence.  

IRON Lab

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Additional researcher: Sean Andrist (Microsoft Research)