Improve model card: Add pipeline tag, links to paper, project page, code, and expanded content

This PR enriches the model card by:
- Adding the `pipeline_tag: robotics` to categorize the model correctly on the Hub.
- Including a link to the associated paper: [Real-to-Sim Robot Policy Evaluation with Gaussian Splatting Simulation of Soft-Body Interactions](https://huggingface.co/papers/2511.04665).
- Providing links to the GitHub repository and project page for more details.
- Expanding the content with the abstract, an overview of the framework, a teaser image, and the citation information from the original GitHub README.

Please review and merge this PR.

README.md

@@ -1,3 +1,36 @@
----
-license: mit
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+---
+license: mit
+pipeline_tag: robotics
+---
+
+# Real-to-Sim Robot Policy Evaluation with Gaussian Splatting Simulation of Soft-Body Interactions
+
+This repository contains the official implementation of the **Real-to-Sim-Policy-Eval** framework, as described in the paper [Real-to-Sim Robot Policy Evaluation with Gaussian Splatting Simulation of Soft-Body Interactions](https://huggingface.co/papers/2511.04665).
+
+**[📚 Paper](https://huggingface.co/papers/2511.04665)** | **[🌐 Project Page](https://real2sim-eval.github.io/)** | **[💻 Code](https://github.com/kywind/real2sim-eval)**
+
+![Teaser Image](https://github.com/kywind/real2sim-eval/raw/main/assets/media/teaser.jpg)
+
+## Abstract
+Robotic manipulation policies are advancing rapidly, but their direct evaluation in the real world remains costly, time-consuming, and difficult to reproduce, particularly for tasks involving deformable objects. Simulation provides a scalable and systematic alternative, yet existing simulators often fail to capture the coupled visual and physical complexity of soft-body interactions. We present a real-to-sim policy evaluation framework that constructs soft-body digital twins from real-world videos and renders robots, objects, and environments with photorealistic fidelity using 3D Gaussian Splatting. We validate our approach on representative deformable manipulation tasks, including plush toy packing, rope routing, and T-block pushing, demonstrating that simulated rollouts correlate strongly with real-world execution performance and reveal key behavioral patterns of learned policies. Our results suggest that combining physics-informed reconstruction with high-quality rendering enables reproducible, scalable, and accurate evaluation of robotic manipulation policies. Website: this https URL
+
+## Overview
+This repository contains the official implementation of the **Real-to-Sim-Policy-Eval** framework. The main components are:
+- Construct simulation assets: visualize object Gaussians, articulate robot Gaussians, define and render different object layouts.
+- Deploy trained [PhysTwin](https://github.com/Jianghanxiao/PhysTwin) to simulate deformable object Gaussians.
+- Train policies using the [policy_training](https://github.com/shuosha/policy_training) submodule located in `policy/`.
+- Evaluate policy in the constructed simulation environment.
+- Interactive control of the Gaussian-based simulation.
+
+For detailed installation instructions and examples on how to run the policy evaluation, keyboard interactive control, and motion replay, please refer to the [GitHub repository](https://github.com/kywind/real2sim-eval).
+
+## Citation
+If you find this repo useful, please consider citing the paper:
+```bibtex
+@article{zhang2025real,
+    title={Real-to-Sim Robot Policy Evaluation with Gaussian Splatting Simulation of Soft-Body Interactions},
+    author={Zhang, Kaifeng and Sha, Shuo and Jiang, Hanxiao and Loper, Matthew and Song, Hyunjong and Cai, Guangyan and Xu, Zhuo and Hu, Xiaochen and Zheng, Changxi and Li, Yunzhu},
+    journal={arXiv preprint arXiv:2511.04665},
+    year={2025}
+}
+```