Add robotics pipeline tag and improve model card

This PR improves the model card for DISC by:
- Adding the `robotics` pipeline tag to the metadata.
- Including direct links to the [DISC paper](https://huggingface.co/papers/2605.20856) and the official [GitHub repository](https://github.com/ReNginx/DISC).
- Adding a sample evaluation command as found in the GitHub README.
- Retaining and organizing the existing checkpoint information and citation.

README.md

@@ -1,5 +1,6 @@
 ---
 license: mit
+pipeline_tag: robotics
 ---
 
 # DISC: Decoupling Instruction from State-Conditioned Control via Policy Generation
@@ -8,23 +9,29 @@ Official implementation of the RSS 2026 paper:
 
 **DISC: Decoupling Instruction from State-Conditioned Control via Policy Generation**
 
+[[Paper](https://huggingface.co/papers/2605.20856)] [[Code](https://github.com/ReNginx/DISC)]
+
 > Hanxiang Ren, Pei Zhou, Xunzhe Zhou, and Yanchao Yang  
 > Robotics: Science and Systems (RSS), 2026
 
-## Overview
+## 🔍 Overview
 
-DISC is a policy-generation framework that decouples high-level language instructions from state-conditioned low-level control. This repository provides training, evaluation, and test-time training code for reproducing the results reported in the paper.
+DISC is a policy-generation framework that decouples high-level language instructions from state-conditioned low-level control. Rather than conditioning a universal policy on language, DISC uses a hypernetwork to generate the entire parameter set of a task-specific visuomotor policy from the instruction alone. This repository provides training, evaluation, and test-time training code for reproducing the results reported in the paper.
 
-## Content
+## 📦 Checkpoints
 
-The repo currently contains two checkpoints to reproduce the results reported in the paper. 
+The repository currently contains two checkpoints to reproduce the results reported in the paper:
 
-- `DISC-libero90.ckpt` contains the model trained on LIBERO-90 dataset (Table 1)
+- `DISC-libero90.ckpt`: Model trained on the LIBERO-90 dataset (Table 1).
+- `DISC-libero_cotrain_spatial_object_goal.ckpt`: Model co-trained on LIBERO-spatial, LIBERO-object, and LIBERO-goal (Table 2).
 
-- `DISC-libero_cotrain_spatial_object_goal.ckpt` contains the model cotrained on LIBERO-sptial, LIBERO-object, LIBERO-goal. (Table 2)
+## 📊 Evaluation
 
-More checkpoints will be uploaded later.
+To evaluate a trained checkpoint on LIBERO-90, you can use the following command from the [official repository](https://github.com/ReNginx/DISC):
 
+```bash
+python train.py --config-name=libero_90_eval model=hylap +ckpt_path=/path/to/checkpoint.ckpt
+```
 
 ## 📝 Citation