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Humans are well-adept at navigating public spaces shared with others, wherecurrent autonomous mobile robots still struggle: while safely and efficientlyreaching their goals, humans communicate their intentions and conform tounwritten social norms on a daily basis; conversely, robots become clumsy inthose daily social scenarios, getting stuck in dense crowds, surprising nearbypedestrians, or even causing collisions. While recent research on robotlearning has shown promises in data-driven social robot navigation,good-quality training data is still difficult to acquire through either trialand error or expert demonstrations. In this work, we propose to utilize thebody of rich, widely available, social human navigation data in many naturalhuman-inhabited public spaces for robots to learn similar, human-like, sociallycompliant navigation behaviors. To be specific, we design an open-sourceegocentric data collection sensor suite wearable by walking humans to providemulti-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 containnumerous natural social navigation interactions; we analyze our dataset,demonstrate its usability, and point out future research directions and usecases.

Toward Human-Like Social Robot Navigation: A Large-Scale, Multi-Modal, Social Human Navigation Dataset