Robot Movement Uncertainty Determines Human Discomfort in Co-worker Scenarios

Daphné Héraïz-Bekkis (1, 2) , Gowrishankar Ganesh (1, 2) , Eiichi Yoshida (2) , Natsuki Yamanobe (2)

1 IDH - Interactive Digital Humans
2 CNRS-AIST JRL - Joint Robotics Laboratory

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'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'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.

Mots clés: human robot interaction; comfort; robot pre- dictability; movement uncertainty; human perception

Domaines: Sciences cognitives; Psychologie

Daphné Héraïz-Bekkis, Gowrishankar Ganesh, Eiichi Yoshida, Natsuki Yamanobe. Robot Movement Uncertainty Determines Human Discomfort in Co-worker Scenarios. ICCAR 2020 - 6th IEEE International Conference on Control, Automation and Robotics, Apr 2020, Singapore, Singapore. pp.59-66, ⟨10.1109/ICCAR49639.2020.9108085⟩. ⟨hal-03009595⟩