BehaviorWorldGen: Closing the Loop between Action Models and World Simulators via Controllable Behavior-Aware Structured World Generation
cs.RO, cs.CV
Submitted: 2026-08-23
Updated: 2026-09-22
Project page: https://behaviorworldgen.github.io
Terminology
Sources
- NVIDIA OmniDreams: Real-Time Generative World Model for Closed-Loop Autonomous Vehicle Simulation
- VLAW: Iterative Co-Improvement of Vision-Language-Action Policy and World Model
- GAIA-1: A Generative World Model for Autonomous Driving
- $\textit{S}^3$Gaussian: Self-Supervised Street Gaussians for Autonomous Driving
- SGDrive: Scene-to-Goal Hierarchical World Cognition for Autonomous Driving
- World Engine: Towards the Era of Post-Training for Autonomous Driving
- DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Driving
- ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving
- Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation
- Flow Matching for Generative Modeling
- World-VLA-Loop: Closed-Loop Learning of Video World Model and VLA Policy
- Unleashing Generalization of End-to-End Autonomous Driving with Controllable Long Video Generation
- SceneStreamer: Continuous Scenario Generation as Next Token Group Prediction
- DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models
- Wan: Open and Advanced Large-Scale Video Generative Models
- ChainFlow-VLA: Causal Flow Planning with Vision-Language Models
- Unified Vision-Language-Action Model
- FLARE: Learning Future-Aware Latent Representations from Vision-Language Models for Autonomous Driving
- CausalDrive: Real-time Causal World Models for Autonomous Driving
- DRAMA: An Efficient End-to-end Motion Planner for Autonomous Driving with Mamba
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- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
- Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving