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About

Wen Fan is a robotics researcher whose work spans robot-assisted microsurgery, tactile sensing, teleoperation, and imitation learning — fields that sit at the intersection of human-robot interaction and intelligent robotic manipulation. His most-cited contribution, a comprehensive 2022 survey on robot-assisted microsurgery (36 citations), established him as a thoughtful synthesizer of advances in autonomous surgical platforms, micro-imaging, and control strategies. Alongside this, Fan has made significant strides in tactile sensor design, developing innovative systems such as ViTacTip (21 citations), MagicTac, and C-Sight — sensors that integrate visual and tactile perception to give robots a richer understanding of physical contact during manipulation. Fan's research also pushes boundaries in human-robot interfaces: his Digital Twin-driven Mixed Reality teleoperation framework (22 citations) demonstrates a compelling vision for immersive, haptic-enabled remote control systems. Meanwhile, his work on one-shot imitation learning (22 citations) addresses practical limitations of data-hungry deep learning models, enabling robots to adapt rapidly to new scenarios. Complementing these contributions, his graph neural network approaches to tactile sensing further enhance interpretability and precision. Collectively, Fan's research reflects a coherent and ambitious agenda: equipping robots with the sensing, learning, and interaction capabilities needed for real-world deployment in both surgical and general manipulation contexts.