Evaluating Remote and Head-worn Eye Trackers in Multi-modal Speech-based HRI

Michael Barz; Peter Poller; Daniel Sonntag

In: Bilge Mutlu; Manfred Tscheligi; Astrid Weiss; James E. Young (Hrsg.). Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction. ACM/IEEE International Conference on Human-Robot Interaction (HRI-17), March 6-9, Vienna, Austria, Pages 79-80, ACM, 3/2017.


Gaze is known to be a dominant modality for conveying spatial information, and it has been used for grounding in human-robot dialogues. In this work, we present the prototype of a gaze-supported multi-modal dialogue system that enhances two core tasks in human-robot collaboration: 1) our robot is able to learn new objects and their location from user instructions involving gaze, and 2) it can instruct the user to move objects and passively track this movement by interpreting the user's gaze. We performed a user study to investigate the impact of different eye trackers on user performance. In particular, we compare a head-worn device and an RGB-based remote eye tracker. Our results show that the head-mounted eye tracker outperforms the remote device in terms of task completion time and the required number of utterances due to its higher precision.

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2017_Evaluating_Remote_and_Head-worn_Eye_Trackers_in_Multi-modal_Speech-based_HRI.pdf (pdf, 537 KB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence