HRI research using autonomous robots in real-world settings can produce results with the highest ecological validity of any study modality, but many difficulties limit such studies' feasibility and effectiveness. We propose Vid2Real HRI, a research framework to maximize real-world insights offered by video-based studies. The Vid2Real HRI framework was used to design an online study using first-person videos of robots as real-world encounter surrogates. The online study ($n = 385$) distinguished the within-subjects effects of four robot behavioral conditions on perceived social intelligence and human willingness to help the robot enter an exterior door. A real-world, between-subjects replication ($n = 26$) using two conditions confirmed the validity of the online study's findings and the sufficiency of the participant recruitment target ($22$) based on a power analysis of online study results. The Vid2Real HRI framework offers HRI researchers a principled way to take advantage of the efficiency of video-based study modalities while generating directly transferable knowledge of real-world HRI. Code and data from the study are provided at https://vid2real.github.io/vid2realHRI

翻译：在真实世界中使用自主机器人进行人机交互研究，能产生具有最高生态效度的研究结果，但诸多困难限制了此类研究的可行性与有效性。我们提出Vid2Real HRI研究框架，旨在最大化基于视频研究提供的真实世界洞察。该框架被用于设计一项在线研究，以机器人第一人称视角视频作为真实世界相遇的替代物。该在线研究（n = 385）区分了四种机器人行为条件对感知社会智能及人类帮助机器人进入外门意愿的受试者内效应。一项采用两种条件的真实世界受试者间复制研究（n = 26）证实了在线研究结果的有效性，以及基于在线研究结果功效分析得出的参与者招募目标（22人）的充分性。Vid2Real HRI框架为人机交互研究者提供了一种原则性方法，既能利用基于视频研究模式的效率，又能生成可直接迁移的真实世界人机交互知识。研究代码与数据见 https://vid2real.github.io/vid2realHRI。