Towards Socially Compliant Navigation in Deep Reinforcement Learning via Proxemics-Based Reward Modeling

2026-08-13

AI Summary

This work addresses the tendency of existing deep reinforcement learning–based navigation methods to prioritize task objectives at the expense of social compliance in dense crowds, often resulting in behaviors that violate human social norms. To mitigate this issue, the authors propose a differentiable reward modeling approach grounded in Hall’s proxemic theory, formalizing personal space as a radial Gaussian mixture field. This formulation enables the computation of a local social cost within the robot’s field of view, which is seamlessly integrated into a deep reinforcement learning framework. The method uniquely translates proxemic theory into a dense, interpretable, and differentiable reward signal, significantly improving social compliance across diverse crowd densities and environments while maintaining navigation efficiency comparable to state-of-the-art approaches.

Abstract

Developing effective robot navigation methods in crowded environments is essential for real-world applications. Although recent deep reinforcement learning (DRL) methods have improved navigation performance in crowded environments, they often focus primarily on task-centric objectives and underrepresent social compliance objectives. In this paper, we introduce a novel proxemics-based reward formulation for DRL social navigation that provides a dense, interpretable social learning signal while maintaining navigation efficiency. Our approach models each human's personal space as a radial Gaussian-mixture field derived from Hall's proxemics theory and computes a robot-centric local cost over the robot's field of view. We integrate the proposed reward into established DRL navigation methods and evaluate it in simulation across multiple crowd scenarios, reward baselines, and crowd densities using both navigation metrics and social metrics. Results show that the proposed reward consistently improves social metrics in simulation while maintaining competitive navigation performance relative to the compared reward models.

Problem

• socially compliant navigation
• crowded environments
• proxemics
• deep reinforcement learning
• social compliance

Innovation

• proxemics
• social navigation
• reward modeling
• deep reinforcement learning
• Gaussian mixture field