| # 🌏 RISE |
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| <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> |
| <a href="https://x.com/jiazhi_yang2024/status/2022247675060797636"><img src="https://img.shields.io/badge/summary-000000?logo=x&logoColor=white" alt="X"></a> |
| </div> |
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| <img src="rise_assets/teaser.gif" alt="RISE demo" width="100%"> |
| </img> |
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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.** |
| Non-trivial 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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| ## 🗺️ Overview |
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| RISE repository is structured with three major parts: |
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| - `policy_and_value/policy_offline_and_value`: OpenPI-based offline policy and value training. They are put together since they share similar model architecture and training pipeline. |
| - `dynamics/dynamics_model`: Action-conditioned dynamics model. |
| - `policy_and_value/policy_online`: Online RL for policy improvement. |
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| ## 🧑💻 Getting Started |
| - [💻 Installation](docs/installation.md) |
| - [🏆 Offline Policy Training & Value Model](docs/offline_learning.md) |
| - [🔮 Dynamics Model](docs/dynamics_model.md) |
| - [🦾 Online Policy Training](docs/online_training.md) |
| - [🛠️ Deployment on Piper](docs/deploy.md) |
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| ## ❤️ Acknowledgement |
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| We thank the following projects for their open-source contributions: [OpenPI Pi0 / Pi05](https://github.com/openpi/openpi), [Genie Envisioner](https://github.com/AgibotTech/Genie-Envisioner), [RLinf](https://github.com/RLinf/RLinf), [LTX-Video](https://github.com/Lightricks/LTX-Video). |
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| ## 📢 News |
| - [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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| [](https://star-history.com/#OpenDriveLab/RISE&Date) |
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