Yuntian Li
Papers
1
Total Citations
11
H-Index
1
About
Yuntian Li is a robotics researcher whose work addresses one of the most persistent challenges in industrial automation: enabling robots to perceive and manipulate transparent objects. Li’s research centers on computer vision, depth perception, and robotic manipulation, with a particular focus on overcoming the limitations of conventional sensors when handling optically challenging materials. Their most cited work, “Transparent Object Depth Perception Network for Robotic Manipulation Based on Orientation-Aware Guidance and Texture Enhancement” (2024, 11 citations), introduces a novel deep learning framework that fuses orientation-aware guidance with texture enhancement to reconstruct accurate depth maps for transparent objects. This contribution is critical because transparent surfaces—common in manufacturing and logistics—typically produce incomplete or noisy depth data, severely hindering robotic grasping and assembly. By developing a network that leverages both geometric cues and texture synthesis, Li has advanced the reliability of robotic perception in real-world industrial settings. Their work bridges the gap between theoretical computer vision and practical robotics, offering tangible improvements for automation systems that must handle everything from glassware to plastic packaging. Li’s research continues to push the boundaries of what robots can perceive, making them a key figure in the evolution of intelligent, sensor-driven manipulation.
Research Focus
Key Achievements
Top Papers
- 1