Robots are increasingly being deployed in public spaces such as shopping malls, sidewalks, and hospitals, where safe and socially aware navigation depends on anticipating how pedestrians respond to their presence. However, existing datasets rarely capture the full spectrum of robot-induced reactions, e.g., avoidance, neutrality, attraction, which limits progress in modeling these interactions. In this paper, we present the Pedestrian-Robot Interaction~(PeRoI) dataset that captures pedestrian motions categorized into attraction, neutrality, and repulsion across two outdoor sites under three controlled conditions: no robot present, with stationary robot, and with moving robot. This design explicitly reveals how pedestrian behavior varies across robot contexts, and we provide qualitative and quantitative comparisons to established state-of-the-art datasets. Building on these data, we propose the Neural Robot Social Force Model~(NeuRoSFM), an extension of the Social Force Model that integrates neural networks to augment inter-human dynamics with learned components and explicit robot-induced forces to better predict pedestrian motion in vicinity of robots. We evaluate NeuRoSFM by generating trajectories on multiple real-world datasets. The results demonstrate improved modeling of pedestrian-robot interactions, leading to better prediction accuracy, and highlight the value of our dataset and method for advancing socially aware navigation strategies in human-centered environments.

翻译：随着机器人在商场、人行道和医院等公共场所的部署日益增多，实现安全且具有社会意识的导航关键在于预知行人对机器人存在的反应。然而，现有数据集很少能完整捕捉机器人引发的反应谱系（例如规避、中立、吸引），这限制了对这些交互行为建模的进展。本文提出了行人-机器人交互（PeRoI）数据集，该数据集在两个户外场景中记录了三种受控条件（无机器人存在、静态机器人存在、移动机器人存在）下被归类为吸引、中立和排斥的行人运动。这一设计明确揭示了行人行为在不同机器人情境下的变化，并与现有先进数据集进行了定性与定量比较。基于这些数据，我们提出了神经机器人社会力模型（NeuRoSFM），该模型扩展了社会力模型，通过集成神经网络来增强人际动力学中的学习组件，并引入显式的机器人诱导力，以更好地预测机器人附近的行人运动。我们在多个真实世界数据集上生成轨迹以评估NeuRoSFM。结果表明，该模型能更好地建模行人-机器人交互，从而获得更高的预测精度，同时凸显了我们的数据集与方法在推进以人为中心环境中的社会意识导航策略方面的价值。