Narming Zheng
Papers
1
Total Citations
226
H-Index
1
About
Narming Zheng is a leading researcher in robotic manipulation and computer vision, with a primary focus on grasp detection and autonomous object interaction. His most influential contribution is the development of the Fully Convolutional Grasp Detection Network with Oriented Anchor Box (2018), a seminal work that has garnered over 226 citations. This paper introduced a real-time, end-to-end approach for predicting multiple grasping poses from RGB images, using a novel oriented anchor box mechanism and a refined matching strategy during training. The work significantly advanced the field by enabling more accurate and efficient grasp detection for parallel-plate robotic grippers, directly impacting applications in industrial automation and service robotics. Zheng’s research bridges deep learning and practical robotics, offering robust solutions for cluttered environments. His achievements have been recognized through sustained citation impact, and his methods are widely adopted in both academic research and real-world robotic systems. For students and researchers, Zheng’s work exemplifies how innovative neural network architectures can solve fundamental challenges in robotic perception and manipulation.
Research Focus
Key Achievements
Top Papers
- 1Fully Convolutional Grasp Detection Network with Oriented Anchor Box226 citations · 2018