Gaze for error detection during human-robot shared manipulation
Workshop Paper, RSS '18 Towards a Framework for Joint Action Workshop, June, 2018
Abstract
Human-robot collaboration systems benefit from the ability of the robot to recognize people’s intentions. People’s nonverbal behavior while performing tasks, especially gaze, has shown to be a reliable signal to recognize what people intend to do. We propose an additional usage of this signal: to recognize when something unexpected has occurred during the task. Case studies from a dataset of gaze behavior when controlling a robot indicate that people’s gaze deviates from ordinary patterns when unexpected conditions occur. By using such a system, robot collaborators can identify unexpected behaviors and smoothly take corrective action.
BibTeX
@workshop{Aronson-2018-113247,author = {Reuben M. Aronson and Henny Admoni},
title = {Gaze for error detection during human-robot shared manipulation},
booktitle = {Proceedings of RSS '18 Towards a Framework for Joint Action Workshop},
year = {2018},
month = {June},
}
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