Jinghong Wang
Papers
1
Total Citations
3
H-Index
1
About
Jinghong Wang is a researcher at the forefront of robotic manipulation and 3D vision, with a focus on enabling robots to perceive and interact with their environments more intelligently. Her work centers on grasp pose generation, a critical challenge in robotics that involves determining how a robot hand should approach and hold an object. In her most-cited paper, "3D Grasp Pose Generation from 2D Anchors and Local Surface" (2022), Wang introduced a novel method that leverages predicted two-dimensional anchors and depth information from the local surface to compute precise three-dimensional grasp poses. This approach improves upon traditional image-based grasp detection by incorporating spatial geometry, allowing robots to handle objects more reliably in cluttered or unstructured settings. Although early in her career, with 3 citations to date, her work represents a meaningful step toward bridging 2D perception and 3D action in robotics. Wang’s research holds promise for applications in industrial automation, assistive robotics, and autonomous systems, and she is recognized for her contributions to advancing practical, data-driven solutions in robotic grasping.
Research Focus
Key Achievements
Top Papers
- 13D Grasp Pose Generation from 2D Anchors and Local Surface3 citations · 2022