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

Asma Atamna 1, 2 Chloé Clavel 2, 1 
2 S2A - Signal, Statistique et Apprentissage
LTCI - Laboratoire Traitement et Communication de l'Information
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 imbalanced 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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Submitted on : Saturday, August 29, 2020 - 9:54:19 PM
Last modification on : Wednesday, November 3, 2021 - 6:20:59 AM
Long-term archiving on: : Monday, November 30, 2020 - 12:08:04 PM


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  • HAL Id : hal-02922102, version 1



Asma Atamna, Chloé Clavel. HRI-RNN: A User-Robot Dynamics-Oriented RNN for Engagement Decrease Detection. 2020. ⟨hal-02922102⟩



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