CoMet: Modeling Group Cohesion for Socially Compliant Robot Navigation in Crowded Scenes
Adarsh Jagan Sathyamoorthy, Utsav Patel, Moumita Paul, Nithish K. Sanjeev Kumar, Yash Ajay Savle, Dinesh Manocha
Abstract
We present CoMet, a novel approach for computing a group’s cohesion and using that to improve a robot’s navigation in crowded scenes. Our approach uses a novel cohesion-metric that builds on prior work in social psychology. We compute this metric by utilizing various visual features of pedestrians from an RGB-D camera on-board a robot. Specifically, we detect characteristics corresponding to the proximity between people, their relative walking speeds, the group size, and interactions between group members. We use our cohesion-metric to design and improve a navigation scheme that accounts for different levels of group cohesion while a robot moves through a crowd. We evaluate the precision and recall of our cohesion-metric on pedestrian datasets. We highlight the performance of our social navigation algorithm on a Turtlebot robot and demonstrate its benefits in terms of multiple metrics: freezing rate (57% decrease), deviation (35.7% decrease), and path length of the trajectory(23.2% decrease).
BibTeX
@inproceedings{ral2022_cometmodelinggro,
title = {CoMet: Modeling Group Cohesion for Socially Compliant Robot Navigation in Crowded Scenes},
author = {Adarsh Jagan Sathyamoorthy and Utsav Patel and Moumita Paul and Nithish K. Sanjeev Kumar and Yash Ajay Savle and Dinesh Manocha},
booktitle = {RA-L 2022},
year = {2022}
}