4 papers · 1 filter
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…
Hide-and-Seek in Trajectories: Discovering Failure Signals for VLA Runtime Monitoring
Seongheon Park, Wendi Li, Changdae Oh +4
Vision-Language-Action (VLA) models enable robots to follow natural language instructions and generalize across diverse tasks, but they remain vulnerable to execution failures that…