Add model card for Mixture of Horizons in Action Chunking

This PR adds a comprehensive model card for the "Mixture of Horizons in Action Chunking" model.

It includes:
- Relevant metadata such as `pipeline_tag`, `license`, and descriptive `tags`.
- A concise introduction to the model based on the paper abstract and GitHub README.
- Links to the paper, project page, and GitHub repository.
- Key figures and a summary of the model's benefits.
- Acknowledgment and citation information.

Please review and merge if everything looks good!

README.md

@@ -0,0 +1,75 @@
+---
+pipeline_tag: robotics
+license: apache-2.0
+tags:
+- reinforcement-learning
+- robotic-manipulation
+- action-chunking
+---
+
+# Mixture of Horizons in Action Chunking
+
+This repository hosts the official implementation of **Mixture of Horizons (MoH)**, introduced in the paper [Mixture of Horizons in Action Chunking](https://huggingface.co/papers/2511.19433).
+
+Vision-language-action (VLA) models for robotic manipulation are highly sensitive to the chosen **action chunk length**, or **horizon**. A fixed horizon presents an inherent trade-off: longer horizons offer superior global foresight but compromise fine-grained accuracy, while shorter ones provide precise local control but struggle with long-term tasks.
+
+To address this challenge, we propose **Mixture of Horizons (MoH)**, a novel, plug-and-play strategy that fuses multiple horizons within a single policy. MoH processes action chunks in parallel segments with different horizons and integrates their outputs. This approach simultaneously leverages long-term foresight and short-term precision with minimal overhead, and enables **Dynamic Inference** through cross-horizon consensus for enhanced efficiency and robustness in complex robotic tasks.
+
+-   📄 [Paper](https://huggingface.co/papers/2511.19433)
+-   📝 [Project Page](https://timsty1.github.io/moh/)
+-   💻 [Code](https://github.com/Timsty1/MixtureOfHorizons/tree/main)
+
+## Introduction
+
+<div align="center">
+  <table border="0" cellspacing="0" cellpadding="0">
+    <tr>
+      <td align="center" width="50%">
+        <img src="https://github.com/Timsty1/MixtureOfHorizons/raw/main/figure/study_of_horizons_pi0.png" alt="Trade-off Effect" width="100%">
+      </td>
+      <td align="center" width="50%">
+        <img src="https://github.com/Timsty1/MixtureOfHorizons/raw/main/figure/intro_motivation_v2.png" alt="Mixture of Horizons" width="100%">
+      </td>
+    </tr>
+    <tr>
+      <td align="center" valign="top">
+        Figure 1: Trade-off between long-term foresight and short-term precision induced by single horizon
+      </td>
+      <td align="center" valign="top">
+        Figure 2: Overview of the proposed mixture-of-horizons strategy
+      </td>
+    </tr>
+  </table>
+</div>
+
+<br>
+
+*   **Mitigates Trade-off**: Addresses the inherent trade-off between long-term foresight and short-term precision induced by single action chunk horizons.
+*   **Plug-and-Play**: Easily integrates into existing full-attention action modules with minimal training or inference overhead.
+*   **Dynamic Inference**: Achieves higher efficiency and robustness by selecting stable actions through cross-horizon consensus.
+
+#### More results on LIBERO
+<div align="center">
+<img src="https://github.com/Timsty1/MixtureOfHorizons/raw/main/figure/libero_main.jpg" width="90%" />
+</div>
+
+## Usage
+
+For detailed instructions on environment setup, training, and evaluation, please refer to the [GitHub repository](https://github.com/Timsty1/MixtureOfHorizons/tree/main).
+
+## ❤️ Acknowledgment
+
+We express our gratitude to [OpenPi](https://github.com/Physical-Intelligence/openpi/tree/main), [LIBERO](https://github.com/Lifelong-Robot-Learning/LIBERO), and [RoboTwin](https://robotwin-platform.github.io/) for their open-source contributions.
+
+## 📝 Citation
+
+If you feel that this paper, models, or codes are helpful, please cite our paper, thanks for your support!
+
+```bibtex
+@article{jing2025mixture_of_horizons,
+  title={Mixture of Horizons in Action Chunking},
+  author={Jing, Dong and Wang, Gang and Liu, Jiaqi and Tang, Weiliang and Sun, Zelong and Yao, Yunchao and Wei, Zhenyu and Liu, Yunhui and Lu, Zhiwu and Ding, Mingyu},
+  journal={arXiv preprint arXiv:2511.19433},
+  year={2025}
+}
+```