Shangbin Guan
Papers
1
Total Citations
15
H-Index
1
About
Shangbin Guan is a researcher advancing the frontiers of intelligent manufacturing and robotic manipulation. His work centers on enhancing robot grasping and sorting capabilities, particularly by overcoming the limitations of traditional single-view systems. Guan’s major contribution is the development of a pushing-grasping collaborative method that integrates a deep Q-network algorithm with dual-viewpoint perception. This approach significantly improves both the efficiency and accuracy of robotic grasping in complex, cluttered environments—a critical challenge for modern automation. His most cited paper, “A Pushing-Grasping Collaborative Method Based on Deep Q-Network Algorithm in Dual Viewpoints” (2022), has garnered 15 citations, reflecting its growing influence in the robotics community. By addressing the shortcomings of 2D camera-based methods, Guan’s work offers a practical pathway toward more adaptive and reliable industrial robots. His research not only pushes the boundaries of reinforcement learning in robotics but also holds tangible promise for smarter, more autonomous manufacturing systems.
Research Focus
Key Achievements
Top Papers
- 1