Understanding human locomotion is crucial for AI agents such as robots, particularly in complex indoor home environments. Modeling human trajectories in these spaces requires insight into how individuals maneuver around physical obstacles and manage social navigation dynamics. These dynamics include subtle behaviors influenced by proxemics - the social use of space, such as stepping aside to allow others to pass or choosing longer routes to avoid collisions. Previous research has developed datasets of human motion in indoor scenes, but these are often limited in scale and lack the nuanced social navigation dynamics common in home environments. To address this, we present LocoVR, a dataset of 7000+ two-person trajectories captured in virtual reality from over 130 different indoor home environments. LocoVR provides accurate trajectory data and precise spatial information, along with rich examples of socially-motivated movement behaviors. For example, the dataset captures instances of individuals navigating around each other in narrow spaces, adjusting paths to respect personal boundaries in living areas, and coordinating movements in high-traffic zones like entryways and kitchens. Our evaluation shows that LocoVR significantly enhances model performance in three practical indoor tasks utilizing human trajectories, and demonstrates predicting socially-aware navigation patterns in home environments.

翻译：理解人类移动行为对于机器人等人工智能体至关重要，尤其是在复杂的室内家庭环境中。对这些空间中人类轨迹进行建模，需要深入理解个体如何规避物理障碍物以及处理社交导航动态。这些动态包括受空间关系学（即空间的社会性使用，如侧身让他人通过或选择更长路线以避免碰撞）影响的微妙行为。先前研究已开发了室内场景中人类运动的数据集，但这些数据集通常规模有限，且缺乏家庭环境中常见的细致社交导航动态。为此，我们提出了LocoVR——一个包含来自130多个不同室内家庭环境的7000余条双人轨迹的虚拟现实数据集。LocoVR提供精确的轨迹数据和准确的空间信息，同时包含丰富的社交驱动移动行为示例。例如，该数据集记录了狭窄空间中个体相互避让、在生活区域调整路径以尊重个人边界，以及在入口和厨房等高流量区域协调移动等场景。我们的评估表明，LocoVR在三个利用人类轨迹的实际室内任务中显著提升了模型性能，并展示了预测家庭环境中社交感知导航模式的能力。