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

• URL: http://arxiv.org/abs/2311.05334v1
• Date: Thu, 9 Nov 2023 13:01:21 GMT
• Title: Real-time Addressee Estimation: Deployment of a Deep-Learning Model on the iCub Robot
• Authors: Carlo Mazzola, Francesco Rea, Alessandra Sciutti
• Abstract summary: Addressee Estimation is a skill essential for social robots to interact smoothly with humans. Inspired by human perceptual skills, a deep-learning model for Addressee Estimation 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.
• Score: 52.277579221741746
• License: http://creativecommons.org/licenses/by/4.0/
• 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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