7 papers · 1 filter
AnyScene: Towards Highly Controllable Driving Scene Generation at Anywhere and Beyond
Haiming Zhang, Junfei Zhou, Feng Jiang +6
Generating high-fidelity and controllable synthetic data is critical for advancing end-to-end autonomous driving, particularly for addressing the long tail of rare safety-critical…
Driving Intents Amplify Planning-Oriented Reinforcement Learning
Hengtong Lu, Victor Shea-Jay Huang, Chengmin Yang +4
Continuous-action policies trained on a single demonstrated trajectory per scene suffer from mode collapse: samples cluster around the demonstrated maneuver and the policy cannot r…
MindVLA-U1: VLA Beats VA with Unified Streaming Architecture for Autonomous Driving
Yuzhou Huang, Benjin Zhu, Hengtong Lu +6
Autonomous driving has progressed from modular pipelines toward end-to-end unification, and Vision-Language-Action (VLA) models are a natural extension of this journey beyond Visio…
Action Emergence from Streaming Intent
Pengfei Jing, Victor Shea-Jay Huang, Hengtong Lu +3
We formalize action emergence as a target capability for end-to-end autonomous driving: the ability to generate physically feasible, semantically appropriate, and safety-compliant…
Dita: Scaling Diffusion Transformer for Generalist Vision-Language-Action Policy
Zhi Hou, Tianyi Zhang, Yuwen Xiong +8
While recent vision-language-action models trained on diverse robot datasets exhibit promising generalization capabilities with limited in-domain data, their reliance on compact ac…
Grounding Actions in Camera Space: Observation-Centric Vision-Language-Action Policy
Tianyi Zhang, Haonan Duan, Haoran Hao +3
Vision-Language-Action (VLA) models frequently encounter challenges in generalizing to real-world environments due to inherent discrepancies between observation and action spaces.…