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Gaze trajectory prediction in the context of social robotics

de La Bourdonnaye, François, Setchi, Rossitza and Zanni-Merk, Cecilia 2016. Gaze trajectory prediction in the context of social robotics. IFAC-PapersOnLine 49 (19) , pp. 126-131. 10.1016/j.ifacol.2016.10.473

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Abstract

Social robotics is an emerging field of robotics that focuses on the interactions between robots and humans. It has attracted much interest due to concerns about an aging society and the need for assistive environments. Within this context, this paper focuses on gaze control and eye tracking as a means for robot control. It aims to improve the usability of human–machine interfaces based on gaze control by developing advanced algorithms for predicting the trajectory of the human gaze. The paper proposes two approaches to gaze-trajectory prediction: probabilistic and symbolic. Both approaches use machine learning. The probabilistic method mixes two state models representing gaze locations and directions. The symbolic method treats the gaze-trajectory prediction problem similar to how word-prediction problems are handled in web browsers. Comparative experiments prove the feasibility of both approaches and show that the probabilistic approach achieves better prediction results.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Engineering
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Uncontrolled Keywords: Social robotics, human–robot interaction, eye tracking, gaze tracking, machine learning, trajectory prediction, time series, teleoperation
Publisher: Elsevier
ISSN: 2405-8963
Date of Acceptance: 2 May 2016
Last Modified: 05 Jun 2017 19:30
URI: http://orca.cf.ac.uk/id/eprint/96883

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