Xuanyang Xi

Papers

Total Citations

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About

Xuanyang Xi’s research lies at the intersection of robot vision, human-robot interaction, and intelligent perception, with a focus on enabling machines to understand and respond to human actions. Xi’s major contributions include advancing prehensile analysis through grasp type understanding—encompassing classification, localization, and clustering—which provides critical insights for robot self-learning and intuitive human-robot collaboration. In the domain of facial recognition, Xi improved the spatially enhanced local binary pattern histogram (eLBPH) method by introducing expression-specific weighting, thereby enhancing identity recognition accuracy for service robots operating in dynamic, real-world environments. Though early in their career, Xi’s work has already garnered attention, with key papers accumulating citations that underscore the growing relevance of these contributions. Notably, Xi’s research bridges computer science, mechanology, and neuroscience, reflecting a multidisciplinary approach that is essential for developing socially aware and adaptive robotic systems. For students and researchers exploring robot perception and interactive intelligence, Xi’s work offers foundational insights into how machines can better interpret human gestures and expressions.