Automatically classifying user engagement for dynamic multi-party human–robot interaction

Foster, M. E. , Gaschler, A. and Giuliani, M. (2017) Automatically classifying user engagement for dynamic multi-party human–robot interaction. International Journal of Social Robotics, 9(5), pp. 659-674. (doi:10.1007/s12369-017-0414-y)

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Abstract

A robot agent designed to engage in real-world human--robot joint action must be able to understand the social states of the human users it interacts with in order to behave appropriately. In particular, in a dynamic public space, a crucial task for the robot is to determine the needs and intentions of all of the people in the scene, so that it only interacts with people who intend to interact with it. We address the task of estimating the engagement state of customers for a robot bartender based on the data from audiovisual sensors. We begin with an offline experiment using hidden Markov models, confirming that the sensor data contains the information necessary to estimate user state. We then present two strategies for online state estimation: a rule-based classifier based on observed human behaviour in real bars, and a set of supervised classifiers trained on a labelled corpus. These strategies are compared in offline cross-validation, in an online user study, and through validation against a separate test corpus. These studies show that while the trained classifiers are best in a cross-validation setting, the rule-based classifier performs best with novel data; however, all classifiers also change their estimate too frequently for practical use. To address this issue, we present a final classifier based on Conditional Random Fields: this model has comparable performance on the test data, with increased stability. In summary, though, the rule-based classifier shows competitive performance with the trained classifiers, suggesting that for this task, such a simple model could actually be a preferred option, providing useful online performance while avoiding the implementation and data-scarcity issues involved in using machine learning for this task.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Foster, Dr Mary Ellen
Authors: Foster, M. E., Gaschler, A., and Giuliani, M.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Journal Name:International Journal of Social Robotics
Publisher:Springer
ISSN:1875-4791
ISSN (Online):1875-4805
Published Online:20 July 2017
Copyright Holders:Copyright © 2017 The Authors
First Published:First published in International Journal of Social Robotics 9(5): 659-974
Publisher Policy:Reproduced under a Creative Commons license

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