V-JEPA Policy: Building Effective World-Action Models on Predictive Visual Latents

arXiv:2609.37250 · cs.CV, cs.AI, cs.RO · Submitted 2026-09-29 · Read on arXiv

cs.CV, cs.AI, cs.RO

Submitted: 2026-09-29

Updated: 2026-09-29

Code: https://github.com/breez3young/VJEPA-Policy

Terminology

• Latent Space — 23× in this paper · explained in 7 episodes
• Benchmark — 21× in this paper · explained in 2 episodes
• Transformer — 12× in this paper · explained in 2 episodes
• Embeddings — 9× in this paper · explained in 2 episodes
• Distribution Shift — 8× in this paper · explained in 5 episodes
• Fine-Tuning — 8× in this paper · explained in 5 episodes
• Generalization — 8× in this paper · explained in 13 episodes
• Robustness — 8× in this paper · explained in 5 episodes
• Generative Model — 5× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Core — 4× in this paper · explained in 2 episodes
• Foundation Model — 4× in this paper · explained in 3 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Flow Matching — 3× in this paper · explained in 7 episodes
• World Model — 3× in this paper · explained in 7 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Self-Supervised Learning — 2× in this paper · explained in 7 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Joint Attention — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Parameter Efficiency — 1× in this paper · explained in 3 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Sample Efficiency — 1× in this paper · explained in 4 episodes
• Temporal Abstraction — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
• Revisiting Feature Prediction for Learning Visual Representations from Video
• GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
• $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
• StarVLA: A Lego-like Codebase for Vision-Language-Action Model Developing
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models
• Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
• Causal World Modeling for Robot Control
• Video Generators are Robot Policies
• JEPA-WAM: Learning Vision-Language-Action Policies with Joint-Embedding World Modeling
• Flow Matching Guide and Code
• LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning
• V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning
• mimic-video: Video-Action Models for Generalizable Robot Control Beyond VLAs
• VLA-JEPA: Enhancing Vision-Language-Action Model with Latent World Model
• Wan: Open and Advanced Large-Scale Video Generative Models
• Qwen-Image Technical Report
• InternVideo3: Agentify Foundation Models with Multimodal Contextual Reasoning
• ABot-M0: VLA Foundation Model for Robotic Manipulation with Action Manifold Learning

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