Papers
12
Total Citations
346
H-Index
8
About
Taobo Cheng is a leading researcher in intelligent robotics, whose work bridges the gap between theoretical control and practical robot autonomy. His core research focuses on dynamic neural network control for redundant manipulators, robot skill learning from human demonstrations, and robust global localization for mobile robots. Cheng’s most influential contribution is his 2020 paper on "Dynamic Neural Networks for Motion-Force Control of Redundant Manipulators," which has garnered 119 citations and addresses the critical challenge of accurate position-force control—a problem that plagues applications like grinding robots where trajectory tracking fails due to impact forces. He further advanced adaptive admittance and kinematic control under model uncertainties, with related works accumulating over 100 citations. Beyond manipulation, Cheng has made notable strides in mobile robotics, developing lidar-visual fusion methods for global localization that enable robots to estimate their pose without prior knowledge, even in sparse-scan environments. His recent work on robot skill learning from complex, long-horizon tasks (30 citations) showcases his commitment to transferring human-inspired skills to robots. With a publication record spanning high-impact venues, Cheng’s research is shaping the next generation of safe, adaptive, and intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
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- 4Global localization of a mobile robot using lidar and visual features35 citations · 2017
- 5A Framework of Robot Skill Learning From Complex and Long-Horizon Tasks30 citations · 2021
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- 7AI based Robot Safe Learning and Control12 citations · 2020
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