  This paper explores the role of eye gaze in human-robot interactions and
proposes a novel system for detecting objects gazed by the human using solely
visual feedback. The system leverages on face detection, human attention
prediction, and online object detection, and it allows the robot to perceive
and interpret human gaze accurately, paving the way for establishing joint
attention with human partners. Additionally, a novel dataset collected with the
humanoid robot iCub is introduced, comprising over 22,000 images from ten
participants gazing at different annotated objects. This dataset serves as a
benchmark for evaluating the performance of the proposed pipeline. The paper
also includes an experimental analysis of the pipeline's effectiveness in a
human-robot interaction setting, examining the performance of each component.
Furthermore, the developed system is deployed on the humanoid robot iCub, and a
supplementary video showcases its functionality. The results demonstrate the
potential of the proposed approach to enhance social awareness and
responsiveness in social robotics, as well as improve assistance and support in
collaborative scenarios, promoting efficient human-robot collaboration. The
code and the collected dataset will be released upon acceptance.

Robotics