Junjie Ming
Papers
1
Total Citations
8
H-Index
1
About
Dr. Junjie Ming is a leading researcher in intelligent robotic assembly, with a primary focus on integrating computer vision, force control, and reinforcement learning to achieve high-precision, flexible automation. His most cited work, "Flexible Gear Assembly with Visual Servoing and Force Feedback" (2023, 8 citations), introduces a pioneering two-stage framework that combines YOLO-based coarse localization with deep reinforcement learning (DRL) for fine insertion. This approach addresses a critical challenge in manufacturing: enabling robots to adapt to part variability and environmental uncertainty without rigid programming. By fusing visual servoing and force feedback, Dr. Ming’s method significantly improves assembly success rates and flexibility, offering a scalable solution for industries requiring delicate, high-tolerance tasks. His contributions bridge the gap between traditional automation and adaptive robotics, demonstrating how DRL can be practically applied to real-world manipulation. As a rising figure in the field, his work is gaining traction among researchers seeking to make assembly lines more autonomous and resilient. Dr. Ming’s research holds promise for advancing smart manufacturing, where robots learn and adjust in real time.
Research Focus
Key Achievements
Top Papers
- 1Flexible Gear Assembly with Visual Servoing and Force Feedback8 citations · 2023