Sangbeom Lee
Papers
1
Total Citations
2
H-Index
1
About
Sangbeom Lee is a leading researcher in robotic perception and manipulation, with a focus on enabling robust grasping in complex, cluttered environments. His work addresses a critical gap in robotics: while deep learning has advanced grasping, most datasets feature simplistic scenes with minimal occlusion. Lee’s major contribution is the introduction of **GraspClutter6D**, a large-scale, real-world dataset designed to train and evaluate perception systems under heavy clutter and diverse object arrangements. This dataset pushes the boundaries of practical robotic grasping, offering unprecedented realism and variability. Though recently published in 2025, GraspClutter6D has already garnered early citations, signaling its potential as a foundational resource in the field. Lee’s research directly tackles the open challenge of transitioning robotic manipulation from controlled labs to unstructured real-world settings, making his work highly influential for students and engineers developing next-generation autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1