Real-time Addressee Estimation: Deployment of a Deep-Learning Model on
  the iCub Robot
Carlo Mazzola Francesco Rea Alessandra Sciutti
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.