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About

Daniel Weber is a researcher specializing in human-robot interaction, gaze-based perception, and augmented reality integration in robotic systems. His work addresses one of the fundamental challenges in robotics: enabling robots to understand and navigate dynamic, real-world environments populated by an effectively infinite variety of objects. Weber's most significant contributions center on leveraging human gaze as a powerful communication channel between humans and robots, allowing robots to identify, localize, and learn about unknown objects without requiring exhaustive pre-programmed object libraries. His 2020 paper on distilling location proposals through gaze information and his 2022 work on augmented reality for robot calibration and eye-based collaboration — each garnering 12 citations — demonstrate his consistent focus on practical, scalable solutions for real-world deployment. His research into gaze-based object detection explores whether robots can meaningfully interpret visual attention data alone to identify and bound objects in unconstrained environments. More recently, his work on multiperspective teaching through shared-gaze multimodal interaction advances collaborative learning frameworks that allow robots to acquire object knowledge dynamically. Together, Weber's contributions represent a coherent and forward-looking research agenda aimed at making human-robot collaboration more intuitive, flexible, and contextually aware.