Humans are well-adept at navigating public spaces shared with others, where current autonomous mobile robots still struggle: while safely and efficiently reaching their goals, humans communicate their intentions and conform to unwritten social norms on a daily basis; conversely, robots become clumsy in those daily social scenarios, getting stuck in dense crowds, surprising nearby pedestrians, or even causing collisions. While recent research on robot learning has shown promises in data-driven social robot navigation, good-quality training data is still difficult to acquire through either trial and error or expert demonstrations. In this work, we propose to utilize the body of rich, widely available, social human navigation data in many natural human-inhabited public spaces for robots to learn similar, human-like, socially compliant navigation behaviors. To be specific, we design an open-source egocentric data collection sensor suite wearable by walking humans to provide multi-modal robot perception data; we collect a large-scale (~100 km, 20 hours, 300 trials, 13 humans) dataset in a variety of public spaces which contain numerous natural social navigation interactions; we analyze our dataset, demonstrate its usability, and point out future research directions and use cases.

翻译：人类在共享公共空间中导航时表现自如，而当前的自主移动机器人仍难以应对此类场景：在安全高效地抵达目标的同时，人类每日通过意图交流并遵循约定俗成的社会规范；相反，机器人在这些日常社交场景中显得笨拙，常被困于密集人群、惊扰路人，甚至引发碰撞。尽管近期机器人学习研究在数据驱动的社交机器人导航方面展现出潜力，但通过试错或专家示范获取高质量训练数据仍十分困难。本文提出利用人类自然栖居的公共场所中丰富且广泛存在的社交人类导航数据，使机器人习得类似的人类化社会合规导航行为。具体而言，我们设计了一套可供行人穿戴的自主视角开源多模态数据采集传感器套件，用于提供机器人感知数据；在包含大量自然社交导航交互的各类公共空间采集大规模数据集（约100公里、20小时、300次试验、13名受试者）；分析并验证该数据集的可用性，同时指出未来研究方向与应用场景。