9 papers
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…
Towards Large Language Models that Benefit for All: Benchmarking Group Fairness in Reward Models
Kefan Song, Jin Yao, Runnan Jiang +2
As Large Language Models (LLMs) become increasingly powerful and accessible to human users, ensuring fairness across diverse demographic groups, i.e., group fairness, is a critical…
Open Vocabulary Monocular 3D Object Detection
Jin Yao, Hao Gu, Xuweiyi Chen +2
We propose and study open-vocabulary monocular 3D detection, a novel task that aims to detect objects of any categores in metric 3D space from a single RGB image. Existing 3D objec…
TrajDeleter: Enabling Trajectory Forgetting in Offline Reinforcement Learning Agents
Chen Gong, Kecen Li, Jin Yao +1
Reinforcement learning (RL) trains an agent from experiences interacting with the environment. In scenarios where online interactions are impractical, offline RL, which trains the…
Machine Unlearning of Pre-trained Large Language Models
Jin Yao, Eli Chien, Minxin Du +4
This study investigates the concept of the `right to be forgotten' within the context of large language models (LLMs). We explore machine unlearning as a pivotal solution, with a f…