Zhenggang Tang
Papers
1
Total Citations
6
H-Index
1
About
Zhenggang Tang is a robotics researcher whose work lies at the intersection of computer vision and manipulation, with a focus on enabling robots to perceive and interact with their environments using minimal sensory input. His key research areas include 3D scene reconstruction, collision-free motion planning, and vision-based robot control. Tang’s major contribution is a pioneering system for collision-free manipulator control that relies solely on RGB camera images—no depth sensors required. By leveraging a NeRF-like reconstruction process, his method builds accurate 3D models of tabletop scenes from multiple RGB views, whether the camera is handheld or mounted on the robot’s end effector. This approach significantly reduces hardware complexity and cost while maintaining robust performance. His most-cited paper, “RGB-Only Reconstruction of Tabletop Scenes for Collision-Free Manipulator Control” (2023, 6 citations), demonstrates this innovation and has already garnered attention for its practical implications in real-world robotics. Tang’s work is notable for pushing the boundaries of perception-driven manipulation, making advanced robotic control more accessible and efficient for applications in manufacturing, service robotics, and autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1