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HRI-RNN: A User-Robot Dynamics-Oriented RNN for Engagement Decrease Detection

Abstract : Natural and fluid human-robot interaction (HRI) systems rely on the robot's ability to accurately assess the user's engagement in the interaction. Current HRI systems for engagement analysis , and more broadly emotion recognition, only consider user data while discarding robot data which, in many cases, affects the user state. We present a novel recurrent neural architecture for online detection of user engagement decrease in a spontaneous HRI setting that exploits the robot data. Our architecture models the user as a distinct party in the conversation and uses the robot data as contextual information to help assess engagement. We evaluate our approach on a real-world highly imbal-anced data set, where we observe up to 2.13% increase in F1 score compared to a standard gated recurrent unit (GRU).
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https://hal.archives-ouvertes.fr/hal-02910344
Contributor : Asma Atamna <>
Submitted on : Saturday, August 1, 2020 - 11:40:27 PM
Last modification on : Tuesday, May 18, 2021 - 2:08:02 PM
Long-term archiving on: : Monday, November 30, 2020 - 12:46:54 PM

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  • HAL Id : hal-02910344, version 1

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Asma Atamna, Chloé Clavel. HRI-RNN: A User-Robot Dynamics-Oriented RNN for Engagement Decrease Detection. INTERSPEECH 2020, Oct 2020, Shanghai, China. ⟨hal-02910344⟩

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