Tingyue Qi
Papers
1
Total Citations
9
H-Index
1
About
Tingyue Qi is a researcher at the forefront of robotic manipulation and intelligent perception, with a particular focus on advancing robot grasping detection through multi-modal learning. Their most-cited work, "A robot grasping detection network based on flexible selection of multi-modal feature fusion structure" (2024), introduces a novel framework that dynamically selects and fuses visual and tactile features to improve grasping accuracy in unstructured environments. This contribution addresses a critical challenge in robotics—how to robustly integrate diverse sensory inputs for real-time decision-making. With 9 citations in a short time, the paper signals growing recognition of Qi's approach to flexible, adaptive architectures that outperform fixed fusion methods. Qi's research sits at the intersection of computer vision, deep learning, and robotics, offering practical solutions for industrial automation and assistive technologies. Their work demonstrates a keen ability to bridge theoretical model design with real-world deployment, making it highly relevant for students and researchers exploring sensor fusion or dexterous manipulation. As Qi continues to publish, their contributions are poised to influence next-generation robotic systems that require reliable, context-aware grasping capabilities.
Research Focus
Key Achievements
Top Papers
- 1