PeRoI：用于学习社交导航中避让、中性与吸引行为的行人-机器人交互数据集
PeRoI: A Pedestrian-Robot Interaction Dataset for Learning Avoidance, Neutrality, and Attraction Behaviors in Social Navigation
AI总结：
PeRoI数据集通过三种控制条件记录行人与机器人交互行为，提出NeuRoSFM模型提升行人-机器人交互预测精度。
摘要：
机器人越来越多地被部署在公共场所，如购物中心、人行道和医院，其中安全且具有社会意识的导航依赖于预测行人对机器人存在反应的方式。然而，现有数据集很少捕捉到机器人引发的所有反应，例如避让、中性、吸引，这限制了对这些交互建模的进步。在本文中，我们提出了行人-机器人交互（PeRoI）数据集，该数据集在两个户外场所下，通过三种受控条件（无机器人、静止机器人、移动机器人）记录行人动作，将其分类为吸引、中性、排斥。这种设计明确揭示了行人行为在机器人环境中的变化，我们提供了与现有最先进数据集的定性和定量比较。基于这些数据，我们提出了Neural Robot Social Force Model（NeuRoSFM），这是Social Force Model的扩展，整合了神经网络以增强人与人之间的动态，并加入显式的机器人引发的力，以更好地预测机器人附近行人运动。我们通过在多个真实世界数据集上生成轨迹来评估NeuRoSFM。结果表明，提高了对行人-机器人交互的建模，导致更好的预测准确性，并突显了我们数据集和方法在推进人类中心环境中社交意识导航策略的价值。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
