AI 中文总结
该研究针对DRL社会导航中社会合规目标不足的问题，提出基于近体学的奖励建模方法，经模拟验证可提升社会指标且保持导航性能。
AI 中文摘要
在拥挤环境中开发有效的机器人导航方法对实际应用至关重要。尽管近期深度强化学习（DRL）方法提升了拥挤环境下的导航性能，但它们通常主要关注以任务为中心的目标，而对社会合规目标的体现不足。本文提出一种新颖的基于近体学（proxemics）的DRL社会导航奖励公式，该公式能提供密集且可解释的社会学习信号，同时保持导航效率。我们的方法将每个人的个人空间建模为由Hall的近体学理论推导而来的径向高斯混合场，并在机器人的视场范围内计算以机器人为中心的局部代价。我们将所提出的奖励集成到已建立的DRL导航方法中，并在模拟环境中针对多种人群场景、奖励基准和人群密度，使用导航指标和社会指标进行评估。结果表明，所提出的奖励在模拟中持续提升社会指标，同时相对于对比的奖励模型保持了具有竞争力的导航性能。
英文摘要
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.
