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We present CoMet, a novel approach for computing a group's cohesion and usingthat to improve a robot's navigation in crowded scenes. Our approach uses anovel cohesion-metric that builds on prior work in social psychology. Wecompute this metric by utilizing various visual features of pedestrians from anRGB-D camera on-board a robot. Specifically, we detect characteristicscorresponding to proximity between people, their relative walking speeds, thegroup size, and interactions between group members. We use our cohesion-metricto design and improve a navigation scheme that accounts for different levels ofgroup cohesion while a robot moves through a crowd. We evaluate the precisionand recall of our cohesion-metric based on perceptual evaluations. We highlightthe performance of our social navigation algorithm on a Turtlebot robot anddemonstrate its benefits in terms of multiple metrics: freezing rate (57%decrease), deviation (35.7% decrease), and path length of the trajectory(23.2%decrease).

CoMet: Modeling Group Cohesion for Socially Compliant Robot Navigation in Crowded Scenes