  Recognizing and understanding conversational groups, or F-formations, is a
critical task for situated agents designed to interact with humans.
F-formations contain complex structures and dynamics, yet are used intuitively
by people in everyday face-to-face conversations. Prior research exploring ways
of identifying F-formations has largely relied on heuristic algorithms that may
not capture the rich dynamic behaviors employed by humans. We introduce REFORM
(REcognize F-FORmations with Machine learning), a data-driven approach for
detecting F-formations given human and agent positions and orientations. REFORM
decomposes the scene into all possible pairs and then reconstructs F-formations
with a voting-based scheme. We evaluated our approach across three datasets:
the SALSA dataset, a newly collected human-only dataset, and a new set of acted
human-robot scenarios, and found that REFORM yielded improved accuracy over a
state-of-the-art F-formation detection algorithm. We also introduce symmetry
and tightness as quantitative measures to characterize F-formations.
Supplementary video: https://youtu.be/Fp7ETdkKvdA , Dataset available at:
github.com/cu-ironlab/Babble
