Group Estimation for Social Robot Navigation in Crowded Environments

Abstract

Socially acceptable navigation in a crowded environment is a challenging problem in robotics due to diverse and unknown human intent. Previous studies have dealt with the social navigation problem in dense crowds via multi-robot collision avoidance. However, it is intractable to follow social compliant trajectory since human-robot interaction differs from the multi-robot collision avoidance problem. To approach our goal, this work exploits a human behavior model and focuses on social group actions such as walking together. We observed that human recognizes the other human groups and avoids them during navigation while maintaining social distances. Based on this observation, this paper proposes a social robot navigation method under group space estimation of crowds on a deep reinforcement learning framework. The proposed method estimates the social groups of crowds based on the behavioral similarities in sensory information. Our reinforcement learning framework learns a socially compliant and effective navigation policy through the proposed human group-aware reward. Our experiment in a crowd simulation demonstrates that the proposed approach generates a human-friendly trajectory with improved navigation performance.