Papers
14
Total Citations
406
H-Index
9
About
Fan Xu is a leading researcher in soft robotics, specializing in the modeling, control, and visual servoing of bioinspired soft robot arms, particularly for underwater applications. Their major contributions include pioneering methods for dynamic visual servoing that correct online distortion and compensate for refraction effects in aquatic environments, enabling precise positioning of octopus-tentacle-like soft manipulators. Xu has advanced shape control using Bézier curve features and cable-driven mechanisms, achieving accurate closed-loop control despite the inherent compliance of soft materials. Their work on adaptive visual servoing and pushing control with active force regulation has pushed the boundaries of safe manipulation in unstructured settings. With papers accumulating over 400 citations—including highly cited works like "Underwater Dynamic Visual Servoing for a Soft Robot Arm" (71 citations) and "Visual Servoing of a Cable-Driven Soft Robot Manipulator With Shape Feature" (69 citations)—Xu’s research has significantly influenced the field. Notably, their recent work on RL-GSBridge explores sim-to-real transfer using 3D Gaussian splatting, demonstrating a commitment to bridging simulation and real-world robotic learning.
Research Focus
Key Achievements
Top Papers
- 1
- 2Visual Servoing of a Cable-Driven Soft Robot Manipulator With Shape Feature69 citations · 2021
- 3Underwater Dynamic Modeling for a Cable-Driven Soft Robot Arm58 citations · 2018
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- 6Soft Robotics: Morphology and Morphology-inspired Motion Strategy38 citations · 2021
- 7
- 8Adaptive visual servoing control for an underwater soft robot18 citations · 2018
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