🤖 WoW-1-Wan-14B-2M

WoW: Towards a World omniscient World model Through Embodied Interaction
Chi et al., 2025 – arXiv:2509.22642

🧠 Key Features

• 14B parameters trained on 2M robot interaction samples
• Learns causal physical reasoning from embodied action
• Generates physically consistent video and robotic action plans
• Uses SOPHIA, a vision-language critic, to refine outputs
• Paired with an Inverse Dynamics Model to complete imagination-to-action loop

🧪 Training Data

• 2M Real-world robot interaction trajectories
• Multimodal scenes including vision, action, and language
• Diverse mixture captions for better generalization

🧠 Mixture Caption Strategy

• Prompt Lengths:
  • Short: "The Franka robot, grasp the red bottle on the table"
  • Long: "The scene... open the drawer, take the screwdriver, place it on the table..."
• Robot Model Mixing:
  • Captions reference various robot types
  • Example: "grasp with the Franka Panda arm", "use end-effector to align"
• Action Granularity:
  • Coarse: "move to object"
  • Fine: "rotate wrist 30° before grasping"

🔄 Continuous Updates

• More trajectories
• Richer language
• Finer multimodal annotations

🧩 Applications

• Zero-shot video generation in robotics
• Causal reasoning and physics simulation
• Long-horizon manipulation planning
• Forward and inverse control prediction

📄 Citation

🔗 Resources

• 🧠 Project page: wow-world-model.github.io
• 💻 GitHub repo: wow-world-model/wow-world-model
• 📊 Dataset: WoW-1 Benchmark Samples