Rujiao Yan

Papers

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About

Rujiao Yan is a researcher in computational audiovisual scene analysis and human-robot interaction. Her work focuses on integrating auditory and visual information to enable robots to understand complex dialog scenarios. In her most-cited paper, "Simple auditory and visual features for human-robot dialog scene analysis" (5 citations), she developed a system that can learn the number of speakers, their locations, and who is currently speaking—all without prior knowledge of the speakers. Her research on "Learning of audiovisual integration" (5 citations) introduced a method for robots to learn the relationship between sound and vision based on temporal and spatial coincidence, even when sounds precede visual signals. This work demonstrates online adaptation of audio-motor maps, allowing robots to adjust their sound localization capabilities in real-time. In "Computational Audiovisual Scene Analysis in Online Adaptation of Audio-Motor Maps" (3 citations), Yan showed that robots can bypass traditional offline calibration by using audiovisual cues to dynamically update their audio-motor maps. Her contributions advance the field of autonomous robotics, enabling more natural and adaptive human-robot communication in dynamic environments.