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

Asma Atamna, ChloƩ Clavel


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).


 DOI: 10.21437/Interspeech.2020-1261

Cite as: Atamna, A., Clavel, C. (2020) HRI-RNN: A User-Robot Dynamics-Oriented RNN for Engagement Decrease Detection. Proc. Interspeech 2020, 4198-4202, DOI: 10.21437/Interspeech.2020-1261.


@inproceedings{Atamna2020,
  author={Asma Atamna and ChloƩ Clavel},
  title={{HRI-RNN: A User-Robot Dynamics-Oriented RNN for Engagement Decrease Detection}},
  year=2020,
  booktitle={Proc. Interspeech 2020},
  pages={4198--4202},
  doi={10.21437/Interspeech.2020-1261},
  url={http://dx.doi.org/10.21437/Interspeech.2020-1261}
}