Improve model card for $\\chi_{0}$

This PR significantly improves the model card for $\\chi_{0}$.

Specifically, it:
- Adds the `robotics` pipeline tag.
- Updates the `license` to `cc-by-nc-sa-4.0`, accurately reflecting the license of the model checkpoints as stated in the GitHub repository.
- Includes key links to the original paper, the project page, and the GitHub repository.
- Provides a concise summary of the $\\chi_{0}$ framework, its technical pillars, and performance highlights.
- Adds relevant tags for better discoverability.
- Removes the irrelevant "File information" section, as instructed.

README.md

@@ -1,3 +1,44 @@
----
-license: cc-by-sa-4.0
----
+---
+license: cc-by-nc-sa-4.0
+pipeline_tag: robotics
+tags:
+  - robotics
+  - robotic-manipulation
+  - garment-manipulation
+  - chi0
+---
+
+# $\\chi_{0}$: Resource-Aware Robust Manipulation via Taming Distributional Inconsistencies
+
+[**Paper**](https://huggingface.co/papers/2602.09021) | [**Project Page**](https://mmlab.hk/research/kai0) | [**GitHub Repository**](https://github.com/OpenDriveLab/KAI0)
+
+$\\chi_{0}$ (pronounced **kai0**) is a resource-efficient framework designed to achieve production-level robustness in robotic manipulation. It addresses the systematic inconsistency and distributional shift among human demonstrations, the inductive bias learned by the policy, and test-time execution, which are primary bottlenecks to real-world robustness in multi-stage tasks.
+
+## Technical Pillars
+
+The $\\chi_{0}$ approach is built upon three technical pillars:
+
+1.  **Model Arithmetic:** A weight-space merging strategy that efficiently soaks up diverse distributions from different demonstrations, varying from object appearance to state variations. This aggregates knowledge without architectural complexity.
+2.  **Stage Advantage:** A stage-aware advantage estimator that provides stable, dense progress signals, overcoming the numerical instability of prior non-stage approaches.
+3.  **Train-Deploy Alignment:** Bridges the distribution gap via spatio-temporal augmentation, heuristic DAgger corrections, and temporal chunk-wise smoothing.
+
+## Performance
+
+$\\chi_{0}$ enables two sets of dual-arm robots to collaboratively orchestrate long-horizon garment manipulation tasks, including flattening, folding, and hanging different clothes. The method exhibits high-reliability autonomy, capable of running the system from an arbitrary initial state for consecutive 24 hours non-stop. Experiments validate that $\\chi_{0}$ surpasses the state-of-the-art $\\pi_{0.5}$ in success rate by nearly 250%, achieved with only 20 hours of data and 8 A100 GPUs.
+
+## License
+
+The code in the repository is licensed under the Apache 2.0 license. The data and model checkpoints are licensed under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).
+
+## Citation
+
+If you find $\\chi_{0}$ useful in your research, please consider citing:
+
+```bibtex
+@article{sima2026kai0,
+  title={$\\chi_{0}$: Resource-Aware Robust Manipulation via Taming Distributional Inconsistencies},
+  author={Yu, Checheng and Sima, Chonghao and Jiang, Gangcheng and Zhang, Hai and Mai, Haoguang and Li, Hongyang and Wang, Huijie and Chen, Jin and Wu, Kaiyang and Chen, Li and Zhao, Lirui and Shi, Modi and Luo, Ping and Bu, Qingwen and Peng, Shijia and Li, Tianyu and Yuan, Yibo},
+  journal={arXiv preprint arXiv:2602.09021},
+  year={2026}
+}
+```