70 citations · 87 across the 19 of their papers we have counts for
4 papers · 2 filters
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…
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…
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…