Create README.md

README.md

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+---
+tags:
+  - robotics
+  - world-model
+  - action-conditioned-video-generation
+  - robot-manipulation
+  - coachworld
+  - arxiv:2609.39685
+---
+
+# CoachWorld — Pretrained Robot World Model
+
+[**Paper**](https://arxiv.org/abs/2609.39685) · [**Code**](https://github.com/RoboCoach-AI/CoachWorld) · [**Project Page & Videos**](https://robocoach-ai.github.io/)
+
+**CoachWorld predicts future robot observations conditioned on visual history and robot actions.** It is the world-model component of **RoboCoach: World Models as Active Coaches for Compositional Robot Skills**.
+
+This repository hosts the **CoachWorld pretrained checkpoint**, trained on heterogeneous single-arm and dual-arm robot data. 
+
+## Overview
+
+CoachWorld is an action-conditioned video world model built on an adapted Wan2.2 TI backbone. It uses visual history and end-effector action conditioning to generate future observations, with camera and action conventions that support learning across robot datasets.
+
+The model supports autoregressive rollout: generated observations become part of the context for subsequent predictions. In the full RoboCoach framework, skill policies interact with the world model, while a separate progress judge identifies subtask failures. These diagnoses guide targeted demonstration collection and policy updates.
+
+## Model at a Glance
+
+| Item | Description |
+| --- | --- |
+| Release | CoachWorld pretrain |
+| Model type | Action-conditioned video world model |
+| Backbone | Adapted Wan2.2 |
+| Conditioning | Visual history and robot end-effector actions, using the prescribed camera/action representation |
+| Output | Predicted future RGB observations |
+| Rollout mode | Autoregressive video prediction |
+| Training scope | Heterogeneous single-arm and dual-arm robot data |
+| Implementation | Custom `coachworld` Python package |
+
+## Getting Started
+
+Download the weights from the [Files and versions](https://huggingface.co/JEdward/CoachWorld/tree/main) tab and use the [CoachWorld code repository](https://github.com/RoboCoach-AI/CoachWorld) for environment setup and model integration.
+
+The implementation includes:
+
+- Model conditioning, inference, and training components.
+- Modified Wan2.2 model and VAE components.
+- Video-latent data interfaces and action/camera conventions.
+- Camera and robot geometry helpers.
+- A distributed training entry point and example configuration.
+
+**Input preparation matters.** Robot actions must follow the implementation’s coordinate-frame, normalization, and temporal-sampling conventions. Raw joint commands or pixel-space trajectories are not interchangeable with the model’s expected conditioning.
+
+Use the CoachWorld implementation to load and run this checkpoint; this release does not claim compatibility with a generic Transformers or Diffusers loading pipeline.
+
+## Citation
+
+If you use CoachWorld in your research, please cite:
+
+```bibtex
+@article{liu2026robocoach,
+  title={RoboCoach: World Models as Active Coaches for Compositional Robot Skills},
+  author={Liu, Jiajun and Chen, Yifan and Liu, Yichao and Zhang, Jiayi
+          and Chen, Ruoqu and Xie, Shaoxuan and Yao, Guocai
+          and Xu, Mengdi and Cui, Sen and Zhang, Changshui},
+  journal={arXiv preprint arXiv:2609.39685},
+  year={2026},
+  url={https://arxiv.org/abs/2609.39685}
+}
+```
+
+## Acknowledgments
+
+CoachWorld builds on Wan2.2 and other open-source components. See the code repository’s [third-party notices](https://github.com/RoboCoach-AI/CoachWorld/blob/main/THIRD_PARTY.md) for component attribution and applicable terms.
+
+## Contact
+
+For implementation questions and reproducibility issues, please open an issue in the [CoachWorld repository](https://github.com/RoboCoach-AI/CoachWorld/issues).