Fan Zhu

University of Hong Kong, Xi’an Jiaotong-Liverpool University

Papers

Total Citations

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About

Fan Zhu is an emerging researcher at the intersection of robotics, intelligent manufacturing, and multimodal sensing. His work focuses on developing sophisticated perception systems that enable robots to interact more intelligently with their physical environment, with a particular emphasis on combining visual and tactile sensory modalities. Zhu's most notable contribution to date is his pioneering work on visuo-tactile sensing for liquid volume estimation, which addresses a deceptively complex challenge in robotic manipulation: understanding the contents of deformable containers during grasping tasks. By fusing RGB camera inputs with tactile sensor data in a deep learning framework — without requiring additional sensor calibration — his approach enables real-time, proprioceptive estimation that brings robots meaningfully closer to human-like dexterity. This work has garnered 10 citations since its 2022 publication, reflecting its relevance to the robotics community. His broader contributions to the field are further evidenced by his involvement in *Advances in Intelligent Manufacturing and Robotics* (2024), signaling an expanding research agenda that bridges perception and manufacturing automation. Though early in his career, Zhu's multidisciplinary approach — combining computer vision, tactile sensing, and deep learning — positions him as a promising voice in next-generation robotic systems research.