Xiaorui Liu

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About

Xiaorui Liu is a researcher at the forefront of social robotics and advanced imaging systems, whose work bridges human-robot interaction and computational optimization. Their key research areas include robot gaze behavior, neural-inspired control systems, and graph-based representation learning for microscopy. Liu’s major contribution lies in developing a control strategy for robot eye-head coordinated gaze behavior, which minimizes neural transmission noise to enable more natural and socially appropriate interactions in human-robot settings—a critical step toward intuitive social robotics. This work, published in 2022, has garnered 27 citations, reflecting its impact on the field. Additionally, Liu has advanced emission source microscopy (ESM) by introducing a novel spherical ESM system driven by graph representation learning and optimization, achieving 14 citations since 2024. This innovation enhances the localization of electromagnetic interference sources in electronic systems, improving scanner accuracy and back-propagation methods. Liu’s publications on social robotics (2021, 2022) further underscore their dedication to creating robots that exhibit lifelike behaviors, making their research highly relevant for students and researchers exploring autonomous systems, human-robot collaboration, and computational imaging.