Real-time Addressee Estimation: Deployment of a Deep-Learning Model on the iCub Robot

Carlo Mazzola, Francesco Rea, Alessandra Sciutti
11/9/2023
cs.ROcs.LG

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Abstract

Addressee Estimation is the ability to understand to whom a person is talking, a skill essential for social robots to interact smoothly with humans. In this sense, it is one of the problems that must be tackled to develop effective conversational agents in multi-party and unstructured scenarios. As humans, one of the channels that mainly lead us to such estimation is the non-verbal behavior of speakers: first of all, their gaze and body pose. Inspired by human perceptual skills, in the present work, a deep-learning model for Addressee Estimation relying on these two non-verbal features is designed, trained, and deployed on an iCub robot. The study presents the procedure of such implementation and the performance of the model deployed in real-time human-robot interaction compared to previous tests on the dataset used for the training.

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Cite this paper

@article{mazzola2023realtime,
  title  = {Real-time Addressee Estimation: Deployment of a Deep-Learning Model on the iCub Robot},
  author = {Carlo Mazzola and Francesco Rea and Alessandra Sciutti},
  year   = {2023},
  eprint = {2311.05334},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.05334v1}
}

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