4 papers
Neglected Free Lunch from Post-training: Progress Advantage for LLM Agents
Changdae Oh, Wendi Li, Seongheon Park +3
Process reward models enable fine-grained, step-level evaluation of LLMs, yet building them for agentic settings remains prohibitively difficult: long-horizon interactions, irrever…
Pose6DAug: Physically Plausible Multi-view Object Swapping for Robot Data Augmentation
Jonghoon Lee, Seong Hyeon Park, Byungwoo Jeon +2
Vision-language-action (VLA) policies have shown strong potential for general-purpose manipulation, yet they often fail on novel, out-of-distribution objects whose appearance or ge…
Dexterous Point Policy: Learning Point-based Dexterous Hand Policies from Human Demonstrations
Beomjun Kim, Seong Hyeon Park, Seunghoon Sim +3
Robotic foundation models pre-trained on human demonstration videos have shown promise, but a significant embodiment gap remains when the resulting policies are deployed on real ro…
Learning Multi-frame and Monocular Prior for Estimating Geometry in Dynamic Scenes
Seong Hyeon Park, Jinwoo Shin
In monocular videos that capture dynamic scenes, estimating the 3D geometry of video contents has been a fundamental challenge in computer vision. Specifically, the task is signifi…