Feifan Zhao
Papers
4
Total Citations
46
H-Index
3
About
Feifan Zhao is a rising researcher at the forefront of intelligent robotic systems, whose work bridges computer vision, human-machine collaboration, and sustainable manufacturing. His primary research areas focus on deep active learning for smart robot vision, augmented reality (AR) integration, and digital twin technologies for robotic disassembly—particularly for end-of-life (EoL) products like electric vehicle batteries. Zhao’s most impactful contribution is his dual-loop implementation architecture for deep active learning and human-machine collaboration, which enables robots to learn "by doing" and adapt to complex, uncertain environments (24 citations). He has also pioneered a teleoperation system that combines AR and digital twins to safely disassemble hazardous EoL batteries, addressing critical challenges such as fire risk and electrical shock (15 citations). His recent work on robust object detection models, using dual constraints of anchors and corners, further advances robotic disassembly by improving accuracy with new or unknown products. Zhao’s innovative integration of AR and digital twins has been recognized as a key enabler for safe, efficient remanufacturing, positioning him as a notable contributor to the circular economy and human-robot interaction fields.
Research Focus
Key Achievements
Top Papers
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