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This paper introduces a proxemics-based reward formulation for deep reinforcement learning (DRL) that enhances socially compliant navigation in crowded environments. By modeling personal space as a radial Gaussian-mixture field, the method provides a dense and interpretable social learning signal while ensuring efficient navigation. Evaluation across various crowd scenarios demonstrates that this approach significantly improves social metrics without sacrificing navigation performance compared to existing reward models.
Robots trained with a proxemics-based reward can navigate crowded spaces more socially aware, improving interactions without compromising efficiency.
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.
