🤖 WoW-1-Wan-14B-2M

WoW-1-Wan-14B is a 14-billion-parameter generative world model trained on 2 million real-world robot interaction trajectories. It is designed to imagine, reason, and act in physically consistent environments, powered by SOPHIA-guided refinement and a co-trained Inverse Dynamics Model.

This model is part of the WoW (World-Omniscient World Model) project, introduced in the paper:

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

This dataset will be continuously updated with:

• 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

@article{chi2025wow,
  title={WoW: Towards a World omniscient World model Through Embodied Interaction},
  author={Chi, Xiaowei and Jia, Peidong and Fan, Chun-Kai and Ju, Xiaozhu and Mi, Weishi and Qin, Zhiyuan and Zhang, Kevin and Tian, Wanxin and Ge, Kuangzhi and Li, Hao and others},
  journal={arXiv preprint arXiv:2509.22642},
  year={2025}
}

🔗 Resources

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

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