Learning a Group-Aware Policy for Robot Navigation
This work is significant for mobile robots operating in human environments, aiming to improve social navigation by respecting human groups.
This paper addresses robot navigation in human environments by developing group-aware policies using deep reinforcement learning. The policies reduce collisions, minimize social norm violations and discomfort, and lessen the robot's impact on pedestrians compared to baseline policies.
Human-aware robot navigation promises a range of applications in which mobile robots bring versatile assistance to people in common human environments. While prior research has mostly focused on modeling pedestrians as independent, intentional individuals, people move in groups; consequently, it is imperative for mobile robots to respect human groups when navigating around people. This paper explores learning group-aware navigation policies based on dynamic group formation using deep reinforcement learning. Through simulation experiments, we show that group-aware policies, compared to baseline policies that neglect human groups, achieve greater robot navigation performance (e.g., fewer collisions), minimize violation of social norms and discomfort, and reduce the robot's movement impact on pedestrians. Our results contribute to the development of social navigation and the integration of mobile robots into human environments.