[Paper Review] Group-Aware Robot Navigation in Crowded Environments.

Kapil D. Katyal, Yuxiang Gao+3 more|arXiv (Cornell University)|Dec 22, 2020

Evacuation and Crowd Dynamics45 references4 citations

TL;DR

This paper proposes a group-aware navigation policy for mobile robots in crowded environments using deep reinforcement learning to model dynamic human groups. By learning to respect group structures, the robot achieves fewer collisions, reduced social norm violations, and lower pedestrian discomfort compared to baseline methods that treat individuals independently.

ABSTRACT

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.

Motivation & Objective

• Address the limitation of prior robot navigation systems that treat pedestrians as independent agents, ignoring group dynamics.
• Improve robot navigation performance in crowded human environments by modeling dynamic human group formations.
• Minimize negative impacts on pedestrians by respecting social norms and group cohesion during robot motion.
• Develop a navigation policy that enhances social compatibility and reduces discomfort in human-robot interactions.

Proposed method

• Employ deep reinforcement learning to train a navigation policy that accounts for dynamic human group formations.
• Model human groups as cohesive units rather than isolated individuals, capturing their spatial and behavioral coherence.
• Use simulation environments to train and evaluate the policy under realistic crowd conditions.
• Incorporate social norms and group integrity into the reward function to guide socially compliant behavior.
• Train the policy end-to-end using imitation and reinforcement learning to balance efficiency and social acceptability.
• Integrate group detection and tracking mechanisms to identify and respect group boundaries in real time.

Experimental results

Research questions

• RQ1 How does modeling human groups improve robot navigation performance in crowded environments?
• RQ2 To what extent does group-aware navigation reduce collisions and social norm violations compared to individual-centric policies?
• RQ3 How does group-aware navigation affect pedestrian comfort and perceived robot behavior?
• RQ4 Can a deep reinforcement learning policy effectively learn to respect dynamic group structures during navigation?

Key findings

• Group-aware policies achieved significantly fewer collisions than baseline policies that ignore group structures.
• The proposed method reduced violations of social norms and minimized discomfort for pedestrians during robot navigation.
• Robot movement had a lower overall impact on pedestrian flow and group cohesion compared to individual-focused navigation.
• Simulation results demonstrated that respecting group dynamics leads to more natural and socially acceptable robot behavior.
• The policy learned to preserve group integrity by avoiding disruptive path crossings or separations.
• Performance improvements were consistent across diverse crowd densities and group configurations.