6 papers · 1 filter
PointWAM: 3D World Action Modeling for Dexterous Robotic Manipulation
Chunghyun Park, Beomjun Kim, Seungcheol Park +5
World action models jointly learn to forecast world dynamics and predict robot actions, such that the learned internal world dynamics guide accurate actions. Existing approaches ty…
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…
Modular Sensory Stream for Integrating Physical Feedback in Vision-Language-Action Models
Jimin Lee, Huiwon Jang, Myungkyu Koo +2
Humans understand and interact with the real world by relying on diverse physical feedback beyond visual perception. Motivated by this, recent approaches attempt to incorporate phy…
Hierarchical Vision Language Action Model Using Success and Failure Demonstrations
Jeongeun Park, Jihwan Yoon, Byungwoo Jeon +6
Prior Vision-Language-Action (VLA) models are typically trained on teleoperated successful demonstrations, while discarding numerous failed attempts that occur naturally during dat…
ContextVLA: Vision-Language-Action Model with Amortized Multi-Frame Context
Huiwon Jang, Sihyun Yu, Heeseung Kwon +3
Leveraging temporal context is crucial for success in partially observable robotic tasks. However, prior work in behavior cloning has demonstrated inconsistent performance gains wh…
Robot-R1: Reinforcement Learning for Enhanced Embodied Reasoning in Robotics
Dongyoung Kim, Sumin Park, Huiwon Jang +3
Large Vision-Language Models (LVLMs) have recently shown great promise in advancing robotics by combining embodied reasoning with robot control. A common approach involves training…