ViDAL: A Visual Dynamics-Grounded Action Latent Space for Vision-Language-Action Models
cs.RO
Submitted: 2026-10-06
Updated: 2026-10-06
Terminology
Sources
- RT-1: Robotics Transformer for Real-World Control at Scale
- OpenVLA: An Open-Source Vision-Language-Action Model
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation
- FAST: Efficient Action Tokenization for Vision-Language-Action Models
- BEAST: Efficient Tokenization of B-Splines Encoded Action Sequences for Imitation Learning
- FASTer: Toward Efficient Autoregressive Vision Language Action Modeling via Neural Action Tokenization
- DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
- V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
- WorldVLA: Towards Autoregressive Action World Model
- FutureVLA: Joint Visuomotor Prediction for Vision-Language-Action Model
- DynVLA: Learning World Dynamics for Action Reasoning in Autonomous Driving
- World Guidance: World Modeling in Condition Space for Action Generation
- Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
- StarVLA- alpha: Reducing Complexity in Vision-Language-Action Systems
- Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
- VLA-JEPA: Enhancing Vision-Language-Action Model with Latent World Model
- Video2Act: A Dual-System Video Diffusion Policy with Robotic Spatio-Motional Modeling
- World Action Models are Zero-shot Policies
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