Papers
7
Total Citations
98
H-Index
5
About
Wupeng Deng is a leading researcher in robotic disassembly and intelligent manufacturing, with a focus on developing autonomous systems for remanufacturing end-of-life products. His major contributions lie in integrating deep learning, predictive modeling, and human-machine collaboration to address the challenges of automated disassembly, particularly for complex items like electric vehicle batteries. Deng’s work on predictive exposure control for vision-based robotic disassembly (28 citations) and a dual-loop deep active learning architecture (24 citations) has advanced smart robot vision, enabling more efficient and adaptive disassembly processes. He also pioneered a two-stage screw detection framework using reflection feature regression (15 citations), which enhances automated visual detection for remanufacturing. Notable achievements include his development of a robotic teleoperation system combining augmented reality and digital twin technologies for safe battery disassembly (15 citations), and a digital twin framework for object location and grasp robots (9 citations). His recent work on memory-gated diffusion policy (2025) and robust end-of-life object detection models (2025) continues to push the boundaries of robotic behaviour learning and adaptability. With over 100 total citations, Deng’s research is pivotal in making remanufacturing economically viable through intelligent automation.
Research Focus
Key Achievements
Top Papers
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- 5Digital Twin System of Object Location and Grasp Robot9 citations · 2020
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