人間らしい社会的ロボットナビゲーションに向けて：大規模マルチモーダル社会的人間ナビゲーションデータセット
Toward Human-Like Social Robot Navigation: A Large-Scale, Multi-Modal, Social Human Navigation Dataset
歩行者の身体に装着できるセンサスイートを開発し、公共空間での人間の社会的ナビゲーションを約100km・20時間分収集した大規模マルチモーダルデータセットを構築・公開した。
著者: Duc M. Nguyen, Mohammad Nazeri, Amirreza Payandeh, Aniket Datar, Xuesu Xiao
分類: cs.RO
原文アブストラクト
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.
