REFORM: Recognizing F-formations for Social Robots

• URL: http://arxiv.org/abs/2008.07668v1
• Date: Mon, 17 Aug 2020 23:32:05 GMT
• Title: REFORM: Recognizing F-formations for Social Robots
• Authors: Hooman Hedayati, Annika Muehlbradt, Daniel J. Szafir, Sean Andrist
• Abstract summary: We introduce REFORM, a data-driven approach for detecting F-formations given human and agent positions and orientations. We find that REFORM yielded improved accuracy over a state-of-the-art F-formation detection algorithm.
• Score: 4.833815605196964
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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