Conference PaperVid2Real HRI: Align video-based HRI study designs with real-world settingsHauser, Elliott; Chan, Yao-Cheng; Modak, Sadanand; Biswas, Joydeep; Hart, JustinHRI 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 (n = 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 vid2real.github.io/vid2realHRI.IEEE2024-08-2610636093542 to 548979-8-3503-7502-2https://doi.org/10.1109/RO-MAN60168.2024.107314132219236Pasadena, CA, USANational Science Foundation