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README.md

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-license: apache-2.0
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+<div align=center>
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+# ⚡️Learning Fused State Representations for Control from Multi-View Observations [ICML 2025]
+
+[![arXiv](https://img.shields.io/badge/arXiv-2502.01316-b31b1b?style=flat&logo=arxiv)](https://arxiv.org/pdf/2502.01316)
+[![Hugging Face](https://img.shields.io/badge/Hugging%20Face-Model-orange?style=flat&logo=huggingface)](https://huggingface.co/datasets/Arya87/MFSC_ICML_2025/tree/main)
+[![Checkpoint](https://img.shields.io/badge/Download-Checkpoint-brightgreen?style=flat&logo=google-drive)](https://huggingface.co/datasets/Arya87/MFSC_ICML_2025/tree/main/MFSC_weights)
+[![Results](https://img.shields.io/badge/Results-Training%20Logs-orange?style=flat&logo=render)](https://huggingface.co/datasets/Arya87/MFSC_ICML_2025/tree/main/MFSC_results)
+[![Project Page](https://img.shields.io/badge/Project-Website-blue?style=flat&logo=github)](https://github.com/zephyr-base/MFSC)
+
+</div>
+
+<p align="center">
+  <img src="framework.jpg" alt="Protein Flow Animation" autoplay loop>
+</p>
+
+## 🧩 Introduction
+This is the code base for our paper on [Learning Fused State Representations for Control from Multi-View Observations]. we propose **M**ulti-view **F**usion **S**tate for **C**ontrol (**MFSC**), firstly incorporating bisimulation metrics learning into MVRL to learn task-relevant representations. Furthermore, we propose a multiview-based mask and latent reconstruction auxiliary task that exploits shared information across views and improves MFSC’s robustness in missing views by introducing a mask token.
+
+## ⚒️ Installation
+For installation and setup instructions for each environment, please refer to the corresponding subdirectories under envs/ or their README files. You may also refer to the setup guidelines from [Keypoint3D](https://github.com/buoyancy99/unsup-3d-keypoints) and [DBC](https://github.com/facebookresearch/deep_bisim4control) for additional reference and compatibility.
+
+## 📖 Run Experiments
+We evaluate our method on a set of 3D manipulation environments **Meta-World**, a high degree of freedom 3D locomotion environment **PyBullet's Ant**, and a more realistic multi-view highway driving scenario, **CARLA**. 
+
+To train MFSC from scratch on each benchmark, simply execute the corresponding .sh script located in its respective directory:
+```
+# Meta-world
+$ bash run.sh
+
+# Pybullet's Ant
+$ bash run.sh
+
+# CARLA
+bash run_local_carla096.sh
+```
+
+## 🚀 Checkpoints and Original Data
+We have made all original training log data, along with intermediate model checkpoints, available in our [Hugging Face repository](https://huggingface.co/datasets/Arya87/MFSC_ICML_2025). We hope this resource is helpful for your experiments and further research.
+
+## 📌 Citation
+If you find this work useful for your research, please consider citing it. 
+```bibtex
+@article{wang2025learning,
+  title={Learning Fused State Representations for Control from Multi-View Observations},
+  author={Wang, Zeyu and Li, Yao-Hui and Li, Xin and Zang, Hongyu and Laroche, Romain and Islam, Riashat},
+  journal={arXiv preprint arXiv:2502.01316},
+  year={2025}
+}
+```
+
+## 👍 Acknowledgments
+Thanks to [Keypoint3D](https://github.com/buoyancy99/unsup-3d-keypoints), [DBC](https://github.com/facebookresearch/deep_bisim4control), [SimSR](https://github.com/bit1029public/SimSR) and [MLR](https://github.com/microsoft/Mask-based-Latent-Reconstruction) for their great work and codebase, which served as the foundation for developing MFSC.
+
+## 📧 Contact Us
+If you have any question, please feel free to contact us via [zywang0824@bit.edu.cn](mailto:zywang0824@bit.edu.cn).