H2O: 人対人の物体受け渡しの視覚的分析のためのベンチマーク
H2O: A Benchmark for Visual Human-human Object Handover Analysis
人から人への物体の受け渡しを視覚的に分析するための大規模データセットH2Oを構築し、受け取り手の把持を予測するRGPNetを提案した。
著者: Ruolin Ye, Wenqiang Xu, Zhendong Xue, Tutian Tang, Yanfeng Wang, Cewu Lu
分類: cs.CV, cs.RO
原文アブストラクト
Object handover is a common human collaboration behavior that attracts attention from researchers in Robotics and Cognitive Science. Though visual perception plays an important role in the object handover task, the whole handover process has been specifically explored. In this work, we propose a novel rich-annotated dataset, H2O, for visual analysis of human-human object handovers. The H2O, which contains 18K video clips involving 15 people who hand over 30 objects to each other, is a multi-purpose benchmark. It can support several vision-based tasks, from which, we specifically provide a baseline method, RGPNet, for a less-explored task named Receiver Grasp Prediction. Extensive experiments show that the RGPNet can produce plausible grasps based on the giver's hand-object states in the pre-handover phase. Besides, we also report the hand and object pose errors with existing baselines and show that the dataset can serve as the video demonstrations for robot imitation learning on the handover task. Dataset, model and code will be made public.
