发表机构
School of Computing, National University of Singapore（新加坡国立大学计算学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对现有社会导航数据集缺乏文化等多样性的问题，引入ACME数据集，通过多国多地多机器人实体大规模收集数据，提供多种数据便于学习与预测，经分析其能捕捉更具挑战场景及更广泛行人行为。
AI 中文摘要
理解机器人和人类在共享空间中的移动方式对于设计有效的社会机器人导航策略和预测人类行为至关重要。然而，现有数据集往往缺乏捕捉文化、地理和人机交互差异所需的多样性，而这些因素强烈影响着适当的社会行为。为填补这一空白，我们引入ACME，这是一个用于社会导航的跨文化、多实体数据集。通过在5个国家的8个地点进行大规模数据收集，使用7种机器人实体，ACME是一个大型多样的多模态数据集，旨在推进社会导航研究，提供29.35小时的机器人车载数据和43.5小时的行人头顶跟踪数据。与先前数据集不同，它专注于在复杂社会场景中通过机器人语音明确的机器人与人群交互来捕捉目标驱动的社会导航行为。为便于学习导航策略和预测行人轨迹，ACME提供3D和2D场景特征、里程计、交互信息以及人工标注的行人轨迹标签。我们通过提供每种传感器模态的人类可读数据以及原始二进制数据，使ACME易于使用。我们的定性和定量分析表明，我们的数据集比以前的数据集捕捉到更具挑战性的场景和更广泛的行人行为分布。
英文摘要
Understanding how robots and humans move in shared spaces is essential for designing effective social robot navigation policies and predicting human behavior. However, existing datasets often lack the diversity needed to capture differences in culture, geography, and human-robot interaction-factors that strongly shape appropriate social behavior. To address this gap, we introduce ACME: A Cross-cultural, Multi-Embodiment dataset for social navigation. A large-scale data collection effort across 8 sites in 5 countries, using 7 robot embodiments, ACME is a large and diverse multi-modal dataset aimed at advancing social navigation research, providing 29.35 hours of onboard robot data and 43.5 hours of overhead pedestrian tracking data. Unlike prior datasets, it focuses on capturing goal-driven social navigation behavior in complex social scenarios with explicit robot-crowd interaction through robot speech. To facilitate learning navigation policies and predicting pedestrian trajectories, ACME provides 3D and 2D scene features, odometry, interaction information, and human-annotated pedestrian trajectory labels. We make ACME easy to use by providing both human-readable data for each sensor modality as well as raw binary data. Our qualitative and quantitative analyses show that our dataset captures more challenging scenarios and a broader distribution of pedestrian behavior than previous datasets.
Comments24 Pages, 19 Figures, Submitted to IJRR on June 29th 2026
