Papers
8
Total Citations
72
H-Index
6
About
Fengjun Bai is a robotics researcher whose work bridges intelligent manipulation, rehabilitation engineering, and human-machine interaction. His primary research areas include robotic grasping, assistive robotics, and biosignal-based control systems. Bai made significant contributions to data-efficient robotic grasping by developing a grasping detection network that incorporates uncertainty estimation for confidence-driven semi-supervised domain adaptation, enabling robots to adapt to new environments with minimal labeled data (30 citations). In rehabilitation robotics, he pioneered muscle force estimation methods using surface EMG signals for lower extremity assistive devices, creating intuitive human-machine interfaces that detect user intention for active assistive robots like the KAAD system. His work on EMG-based jaw muscle force estimation advanced the development of soft oral rehabilitation robots. Bai also contributed to industrial automation through weld quality assessment systems using arc sensing for robotic MIG/TIG welding. His research demonstrates a consistent focus on making robots more adaptive and intuitive—whether through uncertainty-aware grasping, biosignal-driven rehabilitation, or real-time process monitoring—with applications spanning manufacturing, healthcare, and logistics.
Research Focus
Key Achievements
Top Papers
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- 5Weld quality assessment based on arc sensing for robotic welding6 citations · 2015
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