Update README.md

README.md

@@ -17,77 +17,70 @@ size_categories:
 
 # ATG-MoE Pressure-Reducing Valve Assembly Training Set
 
-This repository provides the **training set** used in our paper on multi-skill robotic assembly learning for the **pressure-reducing valve assembly task**.
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+[![Paper: arXiv](https://img.shields.io/badge/arXiv-2603.19029-b31b1b.svg)](https://arxiv.org/abs/2603.19029)
+[![Project Page](https://img.shields.io/badge/Project-Page-blue)](https://hwh23.github.io/ATG-MoE)
+[![GitHub](https://img.shields.io/badge/Code-GitHub-181717?logo=github)](https://github.com/hwh23/ATG-MoE)
 
-The dataset contains **8 assembly skills**, with **96 training episodes per skill**. Each episode is organized in an **RLBench-style format** and includes synchronized **RGB-D observations from four camera views**, robot **proprioception**, camera calibration parameters, keyframe annotations, and natural-language skill instructions.
+This repository provides the **training set** used in our paper: **"ATG-MoE: Autoregressive trajectory generation with mixture-of-experts for assembly skill learning"**. 
 
-> **Note:** This release only contains the **training set**.  
-> Evaluation requires a Unity-based simulation environment. The evaluation scripts and instructions will be released in our GitHub repository.
+The dataset is specifically designed for the **pressure-reducing valve assembly task**, featuring multi-skill robotic learning capabilities.
 
-## Links
-
-- **Paper (arXiv):** https://arxiv.org/abs/2603.19029
-- **Project Page:** https://hwh23.github.io/ATG-MoE
-- **Code / Evaluation Scripts:** https://github.com/hwh23/ATG-MoE
+> [!IMPORTANT]
+> This release only contains the **training set**.  
+> Evaluation requires a Unity-based simulation environment. The evaluation scripts and instructions will be released in our [GitHub repository](https://github.com/hwh23/ATG-MoE).
 
 ---
 
-## Dataset Overview
-
-The dataset is designed for **multi-skill imitation learning** in industrial assembly. It supports training policies that map **multi-view RGB-D observations**, **natural-language instructions**, and **robot states** to skill trajectories.
-
-### Skills
-
-The full dataset includes **8 skills** from the pressure-reducing valve assembly process.
+## 📌 Dataset Overview
 
-- Sleeve Placement
-- Large Spring Insertion
-- Rod Placement
-- Rod Seating
-- Nut Seating
-- Spring Insertion
-- Plug Seating
-- Body Seating
+The dataset supports **multi-skill imitation learning** in industrial assembly. It provides high-quality demonstrations mapping multi-view observations to precise skill trajectories.
 
-### Scale
+### 🛠️ Included Skills
+The dataset covers **8 key skills** required for the assembly process:
+* Sleeve Placement
+* Large Spring Insertion
+* Rod Placement
+* Rod Seating
+* Nut Seating
+* Spring Insertion
+* Plug Seating
+* Body Seating
 
-- **Number of skills:** 8
-- **Training episodes per skill:** 96
-- **Total training episodes:** 768
-
-Each episode corresponds to one folder, e.g. `episode0`, `episode1`, ...
+### 📊 Dataset Scale
+* **Total Training Episodes:** 768
+* **Skills:** 8 distinct assembly tasks
+* **Data per Skill:** 96 training episodes
 
 ---
 
-## Data Format
-
-The dataset structure follows the organization style of **RLBench**.  
-Each `episode` folder contains one demonstration trajectory collected in simulation.
+## 📂 Data Format
 
-An example episode directory is organized as follows:
+The dataset follows the **RLBench** organization style. Each `episode` folder contains a complete demonstration trajectory.
 
+### Directory Structure
 ```text
 episode0/
-├── front_depth/
-├── front_rgb/
-├── left_shoulder_depth/
-├── left_shoulder_rgb/
-├── overhead_depth/
-├── overhead_rgb/
-├── proprioception/
-├── right_shoulder_depth/
-├── right_shoulder_rgb/
-├── camera_matrix.json
-├── kfs.json
-├── lang_emb.pkl
-├── variation_descriptions.json
-└── variation_number.pkl
+├── front_depth/                # Depth maps from front camera
+├── front_rgb/                  # RGB images from front camera
+├── left_shoulder_depth/        # Depth maps from left shoulder camera
+├── left_shoulder_rgb/          # RGB images from left shoulder camera
+├── overhead_depth/             # Depth maps from overhead camera
+├── overhead_rgb/               # RGB images from overhead camera
+├── right_shoulder_depth/       # Depth maps from right shoulder camera
+├── right_shoulder_rgb/         # RGB images from right shoulder camera
+├── proprioception/             # Robot joint states and end-effector pose
+├── camera_matrix.json          # Intrinsic and extrinsic parameters
+├── kfs.json                    # Keyframe indices for trajectory
+├── lang_emb.pkl                # Language instruction embeddings
+├── variation_descriptions.json # Natural language descriptions
+└─�� variation_number.pkl        # Variation ID for the task
+```
 
 ---
 
-## Citation
-
-If you use this dataset or find our work helpful, please cite:
+## 📝 Citation
+If you find this dataset or our work helpful, please cite:
 
 ```bibtex
 @misc{huang2026atgmoeautoregressivetrajectorygeneration,