Shangjin Xie
Papers
2
Total Citations
36
H-Index
2
About
Shangjin Xie is a leading researcher in robotic manipulation, with a primary focus on 7-degree-of-freedom (7-DoF) grasp detection in cluttered environments. His work addresses one of robotics’ most fundamental challenges: enabling robots to precisely predict grasp configurations—including rotation and width—while avoiding collisions in complex scenes. Xie’s most influential contribution, “TransGrasp: A Multi-Scale Hierarchical Point Transformer for 7-DoF Grasp Detection” (2022), has garnered 30 citations and introduces a novel point transformer architecture that captures non-local geometric information, moving beyond traditional hierarchical PointNet++ backbones to significantly improve grasp pose prediction. His subsequent work, “Grasp Region Exploration for 7-DoF Robotic Grasping in Cluttered Scenes” (2023, 6 citations), further advances the field by developing methods to explore high-quality grasp regions in dense clutter. Xie’s research is notable for its practical impact on robot manipulation, offering scalable solutions that bridge the gap between perception and action. His innovative use of multi-scale transformers to process point cloud data represents a key step toward more dexterous and reliable robotic grasping in real-world settings.
Research Focus
Key Achievements
Top Papers
- 1
- 2Grasp Region Exploration for 7-DoF Robotic Grasping in Cluttered Scenes6 citations · 2023