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| license: cc-by-nc-sa-4.0 |
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| # 🌏 RISE |
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| <div id="top" align="left"> |
| <a href="https://opendrivelab.com/rise/"><img src="https://img.shields.io/badge/Proj_Page-blue" alt="Project Page"></a> |
| <a href="https://arxiv.org/abs/2602.11075"><img src="https://img.shields.io/badge/arXiv-2602.11075-b31b1b" alt="arXiv"></a> |
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| Please refer to [RISE repo](https://github.com/OpenDriveLab/RISE_release) for detailed instructions. |
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| ## 🔥 Highlights |
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| <!-- RISE is a self-improving robot policy framework that turns world models into a practical learning environment for real-world manipulation. In short, we make the following three key contributions: --> |
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| - **A compositional world model.** |
| A principled design that combines a controllable multi-view dynamics model with a progress value model, yielding informative advantages for robust policy improvement. |
| - **RL in imagination.** |
| A scalable self-improving framework that bootstraps robot policies through imaginary rollouts, avoiding the hardware cost and laborious reset of real-world interactions. |
| - **Real-world manipulation gains.** |
| Large performance improvements on challenging dexterous tasks, including +35% on dynamic brick sorting, +45% on backpack packing, and +35% on box closing. |
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| ## 📢 News |
| - [2026/05/14] Released sample data in HDF5 and LeRobot formats. |
| - [2026/04/22] Training code and pre-trained dynamics model are released. |
| - [2026/02/11] Paper released on [arXiv](https://arxiv.org/abs/2602.11075). |
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| ## 📄 License and Citation |
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| All assets and code in this repository are under the Apache 2.0 license unless specified otherwise. The data and checkpoint are under CC BY-NC-SA 4.0. Other modules inherit their own distribution licenses. |
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| ```bibtex |
| @article{rise2026, |
| title={RISE: Self-Improving Robot Policy with Compositional World Model}, |
| author={Yang, Jiazhi and Lin, Kunyang and Li, Jinwei and Zhang, Wencong and Lin, Tianwei and Wu, Longyan and Su, Zhizhong and Zhao, Hao and Zhang, Ya-Qin and Chen, Li and Luo, Ping and Yue, Xiangyu and Li, Hongyang}, |
| journal={arXiv preprint arXiv:2602.11075}, |
| year={2026} |
| } |
| ``` |
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