Vid2Real HRI: Align video-based HRI study designs with real-world settings

• Published in: IEEE RO-MAN pp. 542 - 548
• Main Authors: Hauser, Elliott, Chan, Yao-Cheng, Modak, Sadanand, Biswas, Joydeep, Hart, Justin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 26.08.2024
• Subjects: Accuracy; Autonomous robots; Codes; Human-robot interaction; Instruments; Recruitment; Social intelligence; Surveys; Videos
• ISSN: 1944-9437
• DOI: 10.1109/RO-MAN60168.2024.10731413

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 (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.