Making Latent Evolution Explicit: Operator-Structured Transitions for World Action Models
cs.LG, cs.AI, cs.SY, eess.SY
Submitted: 2026-08-27
Updated: 2026-08-27
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- OpenVLA: An Open-Source Vision-Language-Action Model
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
- WorldVLA: Towards Autoregressive Action World Model
- Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning
- Causal World Modeling for Robot Control
- VLA-JEPA: Enhancing Vision-Language-Action Model with Latent World Model
- LaWAM: Latent World Action Models for Efficient Dynamics-Aware Robot Policies
- DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning
- RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- F1: A Vision-Language-Action Model Bridging Understanding and Generation to Actions
- Deep Variational Koopman Models: Inferring Koopman Observations for Uncertainty-Aware Dynamics Modeling and Control
- Koopman Dreamer: Spectrally Constrained Latent Dynamics for Stable World-Model Imagination
- Dream to Control: Learning Behaviors by Latent Imagination
- Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
- FAST: Efficient Action Tokenization for Vision-Language-Action Models
- UniVLA: Learning to Act Anywhere with Task-centric Latent Actions
- Fast-WAM: Do World Action Models Need Test-time Future Imagination?
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