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.

翻译：本文探索了目光注视在人机交互中的作用，并提出了一种仅利用视觉反馈检测人类注视目标的新系统。该系统结合了人脸检测、人类注意力预测与在线目标检测技术，使机器人能够准确感知并解读人类注视方向，为与人类伙伴建立共同注意力奠定了基础。此外，本文还介绍了一个由人形机器人iCub收集的新数据集，包含10位参与者注视不同标注目标的22,000余张图像。该数据集可作为评估所提出流程性能的基准。本文还通过人机交互场景下的实验分析了该流程中各组件的有效性。进一步地，该系统已部署于人形机器人iCub上，并附有展示其功能的补充视频。结果表明，所提方法在增强社交机器人的社会意识与响应能力、改善协作场景中的辅助与支持方面具有潜力，可促进高效的人机协作。代码及收集的数据集将在论文被录用后公开。