Haitao Lin
Papers
1
Total Citations
3
H-Index
1
About
Haitao Lin is an emerging researcher specializing in robotic perception, computer vision, and deep learning, with a particular focus on enabling robots to interact intelligently with their physical environments. His work centers on solving complex visual challenges that directly impact robotic manipulation, including the development of advanced techniques for perceiving occluded objects in cluttered scenes. His most notable contribution, "LAC-Net: Linear-Fusion Attention-Guided Convolutional Network for Accurate Robotic Grasping Under the Occlusion" (2024), tackles one of the field's persistent challenges — amodal segmentation — which empowers robots to infer complete object shapes even when portions are hidden from view. By integrating linear-fusion attention mechanisms with convolutional architectures, Lin's approach pushes the boundaries of what robotic systems can perceive and act upon in real-world, unstructured environments. Though early in his citation trajectory with 3 citations, his research addresses high-impact problems at the intersection of perception and manipulation that are increasingly critical as robotics moves into complex, real-world deployment scenarios. His work represents a promising contribution to the next generation of intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
- 1