Mixing implicit and explicit probes: finding a ground truth for engagement in social human-robot interactions

Authors:
Lee J. Corrigan;Christina Basedow;Dennis Küster;Arvid Kappas;Christopher Peters;Ginevra Castellano
Affiliations:
University Of Birmingham, Birmingham, United Kingdom;Jacobs University Bremen, Bremen, Germany;Jacobs University Bremen, Bremen, Germany;Jacobs University Bremen, Bremen, Germany;Royal Institute of Technology (KTH), Stockholm, Sweden;University Of Birmingham, Birmingham, United Kingdom
Venue:
Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction
Year:
2014

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Abstract

In our work we explore the development of a computational model capable of automatically detecting engagement in social human-robot interactions from real-time sensory and contextual input. However, to train the model we need to establish ground truths of engagement from a large corpus of data collected from a study involving task and social-task engagement. Here, we intend to advance the current state-of-the-art by reducing the need for unreliable post-experiment questionnaires and costly time-consuming annotation with the novel introduction of implicit probes. A non-intrusive, pervasive and embedded method of collecting informative data at different stages of an interaction.