Vid2Real HRI: Align video-based HRI study designs with real-world settings
<|reference_start|>Vid2Real HRI: Align video-based HRI study designs with real-world settings: 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<|reference_end|>
@article{hauser2024vid2real,
    title={Vid2Real HRI: Align video-based HRI study designs with real-world
  settings},
    author={Elliott Hauser, Yao-Cheng Chan, Sadanand Modak, Joydeep Biswas, Justin
  Hart},
    journal={arXiv preprint arXiv:2403.15798},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15798},
    primaryClass={cs.RO cs.HC}
}