@InProceedings{heraizbekkis:iccar:2020,
  author    = {H{\'e}raiz-Bekkis, Daphn{\'e} and Ganesh, Gowrishankar and Yoshida, Eiichi and Yamanobe, Natsuki},
  title     = {Robot Movement Uncertainty Determines Human Discomfort in Co-worker Scenarios},
  booktitle = {IEEE International Conference on Control, Automation and Robotics},
  year      = {2020},
  pages     = {59--66},
  address   = {SIngapore},
  month     = {April 20-April 23},
  url       = {https://staff.aist.go.jp/e.yoshida/papers/ICCAR2020\_Heraiz-Bekkis.pdf},
  keywords  = {human robot interaction; comfort; robot predictability; movement uncertainty; human perception},
  doi       = {10.1109/ICCAR49639.2020.9108085},
  abstract  = {The long term success of a human-robot interaction will depend on how comfortable and safe a human feels with it. But which feature of a robot\textquotesingle s movement determines human comfort? To address this question, here we considered four different models of human discomfort. We then designed an empirical human-robot co-worker task that enables us to both, quantify the discomfort experienced by the human co-worker by analyzing behavioral changes, and examine which model of discomfort explains the changes best. Using this task, we show that the perceived uncertainty in a robot\textquotesingle s movement is a key determinant of human discomfort, and we discuss how movement uncertainty can give a unified explanation for the modulation of human comfort with robots, and trust in them, as reported in several previous studies.}
}