Video Prediction Policy 2: Predict Better, Act Better

arXiv:2610.10270 · cs.CV, cs.RO · Submitted 2026-10-07 · Read on arXiv

cs.CV, cs.RO

Submitted: 2026-10-07

Updated: 2026-10-08

Project page: https://robert-gyj.github.io/video-prediction-policy-2

Terminology

• Generalization — 29× in this paper · explained in 13 episodes
• Benchmark — 18× in this paper · explained in 2 episodes
• Distillation — 16× in this paper · explained in 2 episodes
• Foundation Model — 12× in this paper · explained in 3 episodes
• Alignment — 11× in this paper · explained in 1 episode
• Transformer — 10× in this paper · explained in 2 episodes
• Diffusion Model — 7× in this paper · explained in 8 episodes
• Foundation Models — 7× in this paper · explained in 7 episodes
• Diffusion Models — 6× in this paper · explained in 6 episodes
• World Model — 4× in this paper · explained in 7 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Flow Matching — 3× in this paper · explained in 8 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Action Space — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Consistency Distillation — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• Semantic Understanding — 2× in this paper · explained in 2 episodes
• Vision-Language Model (VLM) — 2× in this paper · explained in 3 episodes
• Zero-Shot Generalization — 2× in this paper · explained in 5 episodes
• AI Agents — 1× in this paper · explained in 2 episodes
• Action Flow — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Closed-Loop Control — 1× in this paper · explained in 3 episodes
• Compositional Generalization — 1× in this paper · explained in 4 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• KV cache — 1× in this paper · explained in 3 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent World Models — 1× in this paper · explained in 2 episodes
• Optical Flow — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Task Planning — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• Cosmos World Foundation Model Platform for Physical AI
• Cosmos 3: Omnimodal World Models for Physical AI
• Scalable Behavior Cloning with Open Data, Training, and Evaluation
• Qwen3-VL Technical Report
• Gen2Act: Human Video Generation in Novel Scenarios enables Generalizable Robot Manipulation
• Zero-Shot Robotic Manipulation with Pretrained Image-Editing Diffusion Models
• $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
• Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets
• AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems
• Seed2.0 Model Card: Towards Intelligence Frontier for Real-World Complexity
• Large Video Planner Enables Generalizable Robot Control
• RoboDojo: A Unified Sim-and-Real Benchmark for Comprehensive Evaluation of Generalist Robot Manipulation Policies
• Scaling Egocentric Vision: The EPIC-KITCHENS Dataset
• RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shot
• Vidar: Embodied Video Diffusion Model for Generalist Manipulation
• Xiaomi-Robotics-1: Scaling Vision-Language-Action Models with over 100K Hours of Real-World Trajectories
• EgoDex: Learning Dexterous Manipulation from Large-Scale Egocentric Video
• Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations
• $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models