To Whom are You Talking? A Deep Learning Model to Endow Social Robots with Addressee Estimation Skills

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Bibliographic Details
Main Authors:	Mazzola, Carlo, Romeo, Marta, Rea, Francesco, Sciutti, Alessandra, Cangelosi, Angelo
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Artificial Intelligence Robotics 68T07, 68T40 I.2.6; I.2.9; I.2.10; J.7
Online Access:	Acceder al recurso
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author	Mazzola, Carlo Romeo, Marta Rea, Francesco Sciutti, Alessandra Cangelosi, Angelo
author_facet	Mazzola, Carlo Romeo, Marta Rea, Francesco Sciutti, Alessandra Cangelosi, Angelo
contents	Communicating shapes our social word. For a robot to be considered social and being consequently integrated in our social environment it is fundamental to understand some of the dynamics that rule human-human communication. In this work, we tackle the problem of Addressee Estimation, the ability to understand an utterance's addressee, by interpreting and exploiting non-verbal bodily cues from the speaker. We do so by implementing an hybrid deep learning model composed of convolutional layers and LSTM cells taking as input images portraying the face of the speaker and 2D vectors of the speaker's body posture. Our implementation choices were guided by the aim to develop a model that could be deployed on social robots and be efficient in ecological scenarios. We demonstrate that our model is able to solve the Addressee Estimation problem in terms of addressee localisation in space, from a robot ego-centric point of view.
format	Preprint
id	arxiv_https___arxiv_org_abs_2308_10757
institution	arXiv
publishDate	2023
record_format	arxiv
spellingShingle	To Whom are You Talking? A Deep Learning Model to Endow Social Robots with Addressee Estimation Skills Mazzola, Carlo Romeo, Marta Rea, Francesco Sciutti, Alessandra Cangelosi, Angelo Machine Learning Artificial Intelligence Robotics 68T07, 68T40 I.2.6; I.2.9; I.2.10; J.7 Communicating shapes our social word. For a robot to be considered social and being consequently integrated in our social environment it is fundamental to understand some of the dynamics that rule human-human communication. In this work, we tackle the problem of Addressee Estimation, the ability to understand an utterance's addressee, by interpreting and exploiting non-verbal bodily cues from the speaker. We do so by implementing an hybrid deep learning model composed of convolutional layers and LSTM cells taking as input images portraying the face of the speaker and 2D vectors of the speaker's body posture. Our implementation choices were guided by the aim to develop a model that could be deployed on social robots and be efficient in ecological scenarios. We demonstrate that our model is able to solve the Addressee Estimation problem in terms of addressee localisation in space, from a robot ego-centric point of view.
title	To Whom are You Talking? A Deep Learning Model to Endow Social Robots with Addressee Estimation Skills
topic	Machine Learning Artificial Intelligence Robotics 68T07, 68T40 I.2.6; I.2.9; I.2.10; J.7
url	https://arxiv.org/abs/2308.10757