6 papers
BiGraspFormer: End-to-End Bimanual Grasp Transformer
Kangmin Kim, Seunghyeok Back, Geonhyup Lee +3
Bimanual grasping is essential for robots to handle large and complex objects. However, existing methods either focus solely on single-arm grasping or employ separate grasp generat…
ManipForce: Force-Guided Policy Learning with Frequency-Aware Representation for Contact-Rich Manipulation
Geonhyup Lee, Yeongjin Lee, Kangmin Kim +4
Contact-rich manipulation tasks such as precision assembly require precise control of interaction forces, yet existing imitation learning methods rely mainly on vision-only demonst…
3D Flow Diffusion Policy: Visuomotor Policy Learning via Generating Flow in 3D Space
Sangjun Noh, Dongwoo Nam, Kangmin Kim +4
Learning robust visuomotor policies that generalize across diverse objects and interaction dynamics remains a central challenge in robotic manipulation. Most existing approaches re…
GraspClutter6D: A Large-scale Real-world Dataset for Robust Perception and Grasping in Cluttered Scenes
Seunghyeok Back, Joosoon Lee, Kangmin Kim +8
Robust grasping in cluttered environments remains an open challenge in robotics. While benchmark datasets have significantly advanced deep learning methods, they mainly focus on si…
High-Quality Unknown Object Instance Segmentation via Quadruple Boundary Error Refinement
Seunghyeok Back, Sangbeom Lee, Kangmin Kim +4
Accurate and efficient segmentation of unknown objects in unstructured environments is essential for robotic manipulation. Unknown Object Instance Segmentation (UOIS), which aims t…
Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise
Yeonguk Yu, Minhwan Ko, Sungho Shin +2
Deep neural networks have demonstrated remarkable performance in various vision tasks, but their success heavily depends on the quality of the training data. Noisy labels are a cri…