Papers
12
Total Citations
595
H-Index
7
About
Dongrui Wu is a leading researcher at the intersection of transfer learning, robotics, and brain-computer interfaces (BCIs). His most influential work includes a landmark survey on negative transfer (331 citations), which systematically analyzes when and why knowledge transfer between domains fails—a critical issue for real-world machine learning applications. Wu has made significant contributions to adaptive control systems for teleoperation and pneumatic muscle actuators, addressing the complexities and uncertainties inherent in human-robot interaction. His research extends to intelligent gait analysis using cane robots and automatic analog instrument reading systems for inspection robots, demonstrating practical applications in healthcare and industrial automation. Wu has also played a pivotal role in advancing BCI technology, organizing and analyzing algorithm contests for motor imagery and calibration-free SSVEP paradigms at the World Robot Contest. His work on human-robot collaboration in construction (2025) represents a forward-looking contribution to the field. With over 500 citations across his most-cited papers, Wu’s research is characterized by its breadth—spanning theoretical foundations of transfer learning to applied robotics and neural interfaces—and its tangible impact on both academic understanding and practical system design.
Research Focus
Key Achievements
Top Papers
- 1A Survey on Negative Transfer331 citations · 2022
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- 6Intelligent Gait Analysis and Evaluation System Based on Cane Robot21 citations · 2022
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