5 papers
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…
GraspSAM: When Segment Anything Model Meets Grasp Detection
Sangjun Noh, Jongwon Kim, Dongwoo Nam +3
Grasp detection requires flexibility to handle objects of various shapes without relying on prior knowledge of the object, while also offering intuitive, user-guided control. This…
Learning to Place Unseen Objects Stably using a Large-scale Simulation
Sangjun Noh, Raeyoung Kang, Taewon Kim +3
Object placement is a fundamental task for robots, yet it remains challenging for partially observed objects. Existing methods for object placement have limitations, such as the re…
Unseen Object Amodal Instance Segmentation via Hierarchical Occlusion Modeling
Seunghyeok Back, Joosoon Lee, Taewon Kim +4
Instance-aware segmentation of unseen objects is essential for a robotic system in an unstructured environment. Although previous works achieved encouraging results, they were limi…