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license: cc-by-nc-sa-4.0
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license: cc-by-nc-sa-4.0
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---
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# 🌏 RISE
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<div id="top" align="left">
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<a href="https://opendrivelab.com/rise/"><img src="https://img.shields.io/badge/Proj_Page-blue" alt="Project Page"></a>
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<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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</div>
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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.**
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A principled design that combines a controllable multi-view dynamics model with a progress value model, yielding informative advantages for robust policy improvement.
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- **RL in imagination.**
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A scalable self-improving framework that bootstraps robot policies through imaginary rollouts, avoiding the hardware cost and laborious reset of real-world interactions.
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- **Real-world manipulation gains.**
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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
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- [2026/04/22] Training code and pre-trained dynamics model are released.
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- [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
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@article{rise2026,
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title={RISE: Self-Improving Robot Policy with Compositional World Model},
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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},
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journal={arXiv preprint arXiv:2602.11075},
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year={2026}
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}
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```
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