Add robotics pipeline tag and improve model card

Hi! I'm Niels from the Hugging Face community team.

I've opened this PR to improve the model card for SeedPolicy. Specifically:
- Added the `robotics` pipeline tag to the metadata to help users discover this model.
- Added a brief overview of the SeedPolicy framework and the SEGA module.
- Included usage instructions for training and evaluating the policy directly from the official GitHub repository.
- Linked the Hugging Face paper page and GitHub repository.

README.md

@@ -1,16 +1,50 @@
 ---
 license: mit
+pipeline_tag: robotics
+tags:
+- robotics
+- imitation-learning
+- diffusion-policy
 ---
-This repository contains the pre-trained model checkpoints for the three typical tasks highlighted in our paper: **SeedPolicy: Horizon Scaling via Self-Evolving Diffusion Policy for Robot Manipulation**.
+
+# SeedPolicy: Horizon Scaling via Self-Evolving Diffusion Policy for Robot Manipulation
+
+This repository contains the pre-trained model checkpoints for the tasks highlighted in the paper **SeedPolicy: Horizon Scaling via Self-Evolving Diffusion Policy for Robot Manipulation**.
 
 ## 📄 Associated Paper & Links
 
-* **Hugging Face Paper Page:** https://huggingface.co/papers/2603.05117
+* **Hugging Face Paper Page:** [https://huggingface.co/papers/2603.05117](https://huggingface.co/papers/2603.05117)
+* **GitHub Repository:** [https://github.com/Youqiang-Gui/SeedPolicy](https://github.com/Youqiang-Gui/SeedPolicy)
 
-* **GitHub Repository:** [https://github.com/Youqiang-Gui/SeedPolicy]
+## 💡 Overview
 
+SeedPolicy introduces **Self-Evolving Gated Attention (SEGA)**, a temporal module that maintains a time-evolving latent state via gated attention. This enables efficient recurrent updates that compress long-horizon observations into a fixed-size representation while filtering irrelevant temporal information. Integrating SEGA into Diffusion Policy (DP) resolves temporal modeling bottlenecks and enables scalable horizon extension for long-horizon robotic manipulation tasks.
 
+## 🛠️ Usage
 
----
+Detailed installation and data generation instructions are available in the [official GitHub repository](https://github.com/Youqiang-Gui/SeedPolicy).
+
+### 1. Train Policy
+```bash
+bash train.sh ${task_name} ${task_config} ${expert_data_num} ${seed} ${action_dim} ${gpu_id} ${config_name}
+
+# Example:
+# bash train.sh beat_block_hammer demo_clean 50 0 14 0 train_diffusion_transformer_hybrid_workspace
+```
+
+### 2. Eval Policy
+```bash
+bash eval.sh ${task_name} ${task_config} ${ckpt_setting} ${expert_data_num} ${seed} ${gpu_id} ${config_name} ${timestamp}
+
+# Example 1: Standard Evaluation
+# bash eval.sh beat_block_hammer demo_clean demo_clean 50 0 0 train_diffusion_transformer_hybrid_workspace "'20260106-143723'"
+
+# Example 2: Generalization Evaluation
+# To evaluate a policy trained on the `demo_clean` setting and tested on the `demo_randomized` setting, run:
+# bash eval.sh beat_block_hammer demo_randomized demo_clean 50 0 0 train_diffusion_transformer_hybrid_workspace "'20260106-143723'"
+```
+
+The evaluation results, including videos, will be saved in the `eval_result` directory under the project root.
 
-*(For detailed installation, usage instructions, and data generation, please refer to our main GitHub repository.)*
+## 😺 Acknowledgements
+Our code is generally built upon: [Diffusion Policy](https://github.com/real-stanford/diffusion_policy) and [RoboTwin 2.0](https://github.com/RoboTwin-Platform/RoboTwin). Specifically, the implementation of our state update code references [CUT3R](https://github.com/CUT3R/CUT3R) and [TTT3R](https://github.com/Inception3D/TTT3R).