Predicting Consequences and Reinforcing Navigation Policies with Latent World Models
cs.AI
Submitted: 2026-08-23
Updated: 2026-08-23
Project page: https://wzm206.github.io/latent-world-model-nav
Terminology
Sources
- WorldEval: World Model as Real-World Robot Policies Evaluator
- World Action Models are Zero-shot Policies
- Dream to Control: Learning Behaviors by Latent Imagination
- PaLM-E: An Embodied Multimodal Language Model
- Wan: Open and Advanced Large-Scale Video Generative Models
- Cosmos World Foundation Model Platform for Physical AI
- HunyuanVideo: A Systematic Framework For Large Video Generative Models
- Seedance 1.0: Exploring the Boundaries of Video Generation Models
- GAIA-1: A Generative World Model for Autonomous Driving
- How Far is Video Generation from World Model: A Physical Law Perspective
- Advancing Open-source World Models
- 3D-VLA: A 3D Vision-Language-Action Generative World Model
- DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning
- DINOv2: Learning Robust Visual Features without Supervision
- World Models
- Mastering Atari with Discrete World Models
- PWM: Policy Learning with Multi-Task World Models
- World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training
- Robotic World Model: A Neural Network Simulator for Robust Policy Optimization in Robotics
- WorldVLA: Towards Autoregressive Action World Model
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