Kangmin Kim
Papers
1
Total Citations
2
H-Index
1
About
Kangmin Kim is a robotics researcher whose work tackles the fundamental challenge of enabling robots to perceive and grasp objects in complex, cluttered environments. His key research areas include robotic manipulation, 3D perception, and deep learning for grasping. Kim’s major contribution is the creation of GraspClutter6D, a large-scale, real-world dataset that addresses a critical gap in existing benchmarks. While prior datasets focused on simplistic scenes with light occlusion, GraspClutter6D provides diverse, heavily cluttered scenarios that better reflect practical, real-world conditions. This work has already garnered attention, with early citations reflecting its significance for advancing robust robotic grasping. By providing a more challenging and realistic testbed, Kim’s research directly supports the development of perception and grasping algorithms that can operate reliably outside controlled lab settings. His contributions are particularly valuable for applications in warehouse automation, domestic service robots, and industrial manufacturing, where robots must handle unpredictable clutter. Kim’s dataset and findings are poised to become a standard resource for researchers aiming to bridge the gap between simulation and real-world robotic performance.
Research Focus
Key Achievements
Top Papers
- 1