  This research report explores the role of eye gaze in human-robot
interactions and proposes a learning system for detecting objects gazed at by
humans using solely visual feedback. The system leverages 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 the field of human gaze estimation in table-top human-robot
interaction (HRI) contexts. In this work, we use it to evaluate the performance
of the proposed pipeline and examine the performance of each component.
Furthermore, the developed system is deployed on the iCub, and a supplementary
video showcases its functionality. The results demonstrate the potential of the
proposed approach as a first step to enhance social awareness and
responsiveness in social robotics, as well as improve assistance and support in
collaborative scenarios, promoting efficient human-robot collaboration.
