Heeseon Rho
Papers
1
Total Citations
2
H-Index
1
About
Heeseon Rho is a robotics researcher advancing the frontier of autonomous manipulation in complex, real-world environments. Her primary focus lies in robust perception and grasping, specifically addressing the critical challenge of enabling robots to reliably interact with objects in highly cluttered, unstructured scenes. Rho’s most notable contribution is the creation of **GraspClutter6D**, a large-scale, real-world dataset designed to overcome the limitations of existing benchmarks, which often feature simplistic, lightly occluded settings. By providing diverse, heavily cluttered scenarios, this work pushes deep learning methods toward practical, deployable solutions. Though recently published (2025), this foundational dataset has already garnered early citations, signaling its potential to become a standard resource in the field. Rho’s research directly tackles a bottleneck in industrial and service robotics, where the ability to grasp objects from a messy bin or table is essential. Her work is particularly valuable for students and researchers seeking to train and evaluate perception systems that must perform reliably outside the lab, bridging the gap between controlled experiments and the unpredictable messiness of the real world.
Research Focus
Key Achievements
Top Papers
- 1