Wenqi Li
Papers
1
Total Citations
2
H-Index
1
About
Wenqi Li is a researcher whose work sits at the intersection of robotics, manufacturing automation, and artificial intelligence. His key research areas include textile manufacturing robotics, deep reinforcement learning applications, and precision automation systems for industrial production lines. Li’s major contribution lies in pioneering the application of combined deep reinforcement learning to the design and control of 3-DOF punching robots for textile upper manufacturing—a critical process that determines positioning accuracy in shoemaking. His most-cited paper, "The Study of a Textile Punching Robot Based on Combined Deep Reinforcement Learning" (2018), addresses a significant gap in the field by developing intelligent control methods for punching force optimization. While his citation count is still growing, Li’s work represents an important step toward integrating modern AI techniques into traditional manufacturing processes, potentially transforming how precision tasks are automated in the textile industry. His research bridges the gap between theoretical reinforcement learning and practical industrial applications, offering valuable insights for both robotics engineers and manufacturing researchers seeking to improve production efficiency through intelligent automation.
Research Focus
Key Achievements
Top Papers
- 1