Papers
26
Total Citations
747
H-Index
12
About
Sheng Xu is a versatile robotics and artificial intelligence researcher whose work spans precision agriculture, robotic manipulation, medical robotics, and learning-based control systems. His most influential contribution, a 2022 review of deep learning in multiscale agricultural sensing (232 citations), established him as a leading voice in applying AI to address global food security challenges exacerbated by climate change and the COVID-19 pandemic. Complementing this, his 2024 survey on UAVs and deep learning in precision agriculture further cements his commitment to sustainable farming technologies. In robotics, Xu has made significant strides in grasp pose estimation, developing a monocular RGB-D approach for 7-DoF object grasping (117 citations) and advancing reactive grasping for dynamic objects. His contributions to medical robotics include pioneering work on magnetically controlled guidewire systems for vascular intervention (88 citations) and an early review of soft robotics in medical applications (39 citations). Throughout his career, Xu has consistently championed learning-based control strategies — including broad learning systems and learning-from-demonstration methods — to simplify complex robot trajectory tracking and motion control. His diverse, high-impact publication record reflects a researcher bridging fundamental machine learning with real-world robotic and agricultural applications.
Research Focus
Key Achievements
Top Papers
- 1A Review of Deep Learning in Multiscale Agricultural Sensing232 citations · 2022
- 2RGB Matters: Learning 7-DoF Grasp Poses on Monocular RGBD Images117 citations · 2021
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- 4Soft Robotics in Medical Applications39 citations · 2018
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- 6Robot trajectory tracking control using learning from demonstration method32 citations · 2019
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- 10Target-referenced Reactive Grasping for Dynamic Objects16 citations · 2023