243 citations · 320 across the 14 of their papers we have counts for
14 papers
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…
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…
Measurement of reactor antineutrino oscillation at SNO+
SNO+ Collaboration, :, M. Abreu +298
The SNO+ collaboration reports its second spectral analysis of reactor antineutrino oscillation using 286 tonne-years of new data. The measured energies of reactor antineutrino can…
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…
Domain-Specific Block Selection and Paired-View Pseudo-Labeling for Online Test-Time Adaptation
Yeonguk Yu, Sungho Shin, Seunghyeok Back +3
Test-time adaptation (TTA) aims to adapt a pre-trained model to a new test domain without access to source data after deployment. Existing approaches typically rely on self-trainin…
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…
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…
SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive Learning
Seongju Lee, Yeonguk Yu, Seunghyeok Back +2
Automatic sleep scoring is essential for the diagnosis and treatment of sleep disorders and enables longitudinal sleep tracking in home environments. Conventionally, learning-based…
Automatic Detection of Injection and Press Mold Parts on 2D Drawing Using Deep Neural Network
Junseok Lee, Jongwon Kim, Jumi Park +3
This paper proposes a method to automatically detect the key feature parts in a CAD of commercial TV and monitor using a deep neural network. We developed a deep learning pipeline…
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…
Object Detection for Understanding Assembly Instruction Using Context-aware Data Augmentation and Cascade Mask R-CNN
Joosoon Lee, Seongju Lee, Seunghyeok Back +2
Understanding assembly instruction has the potential to enhance the robot s task planning ability and enables advanced robotic applications. To recognize the key components from th…
Segmenting Unseen Industrial Components in a Heavy Clutter Using RGB-D Fusion and Synthetic Data
Seunghyeok Back, Jongwon Kim, Raeyoung Kang +2
Segmentation of unseen industrial parts is essential for autonomous industrial systems. However, industrial components are texture-less, reflective, and often found in cluttered an…
Intra- and Inter-epoch Temporal Context Network (IITNet) Using Sub-epoch Features for Automatic Sleep Scoring on Raw Single-channel EEG
Hogeon Seo, Seunghyeok Back, Seongju Lee +3
A deep learning model, named IITNet, is proposed to learn intra- and inter-epoch temporal contexts from raw single-channel EEG for automatic sleep scoring. To classify the sleep st…