5 papers · 1 filter
GeoWAM: Visual Geometry World Action Models for Autonomous Driving
Yiren Lu, Xin Ye, Jiaming Liu +10
World action models (WAMs) have recently gained increasing attention as a framework for jointly modeling scene evolution and ego actions in autonomous driving. Most existing WAMs l…
TraVEL: Trajectory-Guided Video Embedding Learning for Driving-Video Retrieval
Yi-Chung Chen, Philip Jacobson, Tom Lampo +6
Efficiently retrieving relevant clips from large-scale driving logs is essential for data curation, model development, and safety analysis. Structured and rule-based retrieval syst…
VLGA: Vision-Language-Geometry-Action Models for Autonomous Driving
Jin Yao, Dhruva Dixith Kurra, Tom Lampo +3
Vision-language-action (VLA) models can describe scenes and reason about them in language, yet still struggle to ground their actions in the dense 3D world around them. Existing ap…
GeoAlign: Beyond Semantics with State-Guided Spatial Alignment in VLA Models
Yizhi Chen, Zhanxiang Cao, Xinyi Peng +14
Current Vision-Language-Action (VLA) models often optimize for semantic grounding, whereas executable manipulation requires geometry-aware spatial alignment. We introduce GeoAlign,…
LabelAny3D: Label Any Object 3D in the Wild
Jin Yao, Radowan Mahmud Redoy, Sebastian Elbaum +2
Detecting objects in 3D space from monocular input is crucial for applications ranging from robotics to scene understanding. Despite advanced performance in the indoor and autonomo…