Geonwoo Kwon
Papers
1
Total Citations
5
H-Index
1
About
Geonwoo Kwon is a researcher advancing the field of robotic manipulation through intelligent tactile sensing and machine learning. His primary research areas include slip detection, tactile sensor arrays, and deep learning for robotic grasping. Kwon’s most notable contribution is his work on a multilayer-perceptron-based slip detection algorithm, which uses only normal force data from sensor arrays to enable robotic grippers to autonomously grasp unknown objects. This approach, published in 2023, has already garnered 5 citations, highlighting its relevance and early impact in the robotics community. By focusing on frequency-based normal force analysis, Kwon’s method offers a practical, high-performance solution for a critical challenge in autonomous manipulation. His work bridges the gap between tactile sensing and real-time control, making robotic grasping more reliable and adaptive. For students and researchers interested in the intersection of robotics, sensor technology, and deep learning, Kwon’s research provides a clear pathway toward more dexterous and autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
- 1