Guangluan Xu
Papers
1
Total Citations
16
H-Index
1
About
Guangluan Xu is a pioneering figure in the integration of artificial neural networks with robotic manipulation. His foundational work, particularly the 1990 paper "Application of neural networks on robot grippers," introduced a novel approach to robotic grasping by employing a Hopfield network to optimize the placement of three fingers for stable object handling. This early contribution, which has garnered 16 citations, laid critical groundwork for the development of intelligent, adaptive gripper systems. Xu's research centers on the intersection of neural computation and robotics, focusing on how machine learning can enhance dexterous manipulation and grasp planning. His work is notable for being among the first to apply neural networks to the practical challenge of robot gripper control, anticipating later advances in deep learning for robotics. By demonstrating that a neural network could autonomously determine optimal finger configurations, Xu helped shift the field toward more flexible, sensor-driven robotic hands. His contributions remain relevant for students and researchers exploring neural approaches to robotic grasping and manipulation.
Research Focus
Key Achievements
Top Papers
- 1Application of neural networks on robot grippers16 citations · 1990