Jingfei Wang
Papers
2
Total Citations
30
H-Index
2
About
Jingfei Wang is a rising researcher at the forefront of intelligent manufacturing, specializing in human-robot collaboration (HRC) and reinforcement learning. Her work addresses a critical challenge in modern assembly: how to dynamically allocate tasks between humans and machines to maximize both efficiency and worker satisfaction. Wang’s most notable contribution is the development of a transfer reinforcement learning and digital-twin based task allocation method, which uses real-time simulation to adapt assembly workflows—a paper that has already garnered 21 citations since its 2025 publication. She further advanced the field by integrating human preference into the decision-making process through reinforcement learning from human feedback (RLHF), a novel approach that ensures robotic systems respect individual worker comfort and skill levels. This second paper, also from 2025, has earned 9 citations and highlights her commitment to human-centric automation. By bridging digital twin technology with adaptive learning algorithms, Wang is laying the groundwork for more intuitive, responsive, and collaborative industrial environments. Her work is particularly impactful for students and researchers exploring the intersection of AI, ergonomics, and smart manufacturing.
Research Focus
Key Achievements
Top Papers
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- 2