1 citations · 1 across the 6 of their papers we have counts for
6 papers
HuRo: Robotizing Human Videos for Scalable VLA Pretraining
Jinho Jeong, Se June Joo, Jaehyun Kang +4
Human video datasets offer an abundant and diverse source of interaction data that can complement expensive real-robot data. To bridge the human-to-robot embodiment gap, existing a…
RLDX-1 Technical Report
Dongyoung Kim, Huiwon Jang, Myungkyu Koo +65
While Vision-Language-Action models (VLAs) have shown remarkable progress toward human-like generalist robotic policies through the versatile intelligence (i.e. broad scene underst…
UniSkill: Imitating Human Videos via Cross-Embodiment Skill Representations
Hanjung Kim, Jaehyun Kang, Hyolim Kang +3
Mimicry is a fundamental learning mechanism in humans, enabling individuals to learn new tasks by observing and imitating experts. However, applying this ability to robots presents…
: Scalable Auto-Feedback for LLM-based Chart Generation
Woosung Koh, Jang Han Yoon, MinHyung Lee +7
Generating high-quality charts with Large Language Models (LLMs) presents significant challenges due to limited data and the high cost of scaling through human curation. $\langle \…
VISAGE: Video Instance Segmentation with Appearance-Guided Enhancement
Hanjung Kim, Jaehyun Kang, Miran Heo +3
In recent years, online Video Instance Segmentation (VIS) methods have shown remarkable advancement with their powerful query-based detectors. Utilizing the output queries of the d…
Leveraging Image Augmentation for Object Manipulation: Towards Interpretable Controllability in Object-Centric Learning
Jinwoo Kim, Janghyuk Choi, Jaehyun Kang +3
The binding problem in artificial neural networks is actively explored with the goal of achieving human-level recognition skills through the comprehension of the world in terms of…