Update README.md

README.md

@@ -1,12 +1,25 @@
+---
+tags:
+- Embodied-AI
+- Robotics
+- Offline-to-Online-RL
+- Value-Function
+- VLA-Model
+- Robot-Foundation-Model
+- Vision-Language-Action
+- Robot-Learning
+- Imitation-Learning
+---
+
 # Robo-ValueRL
 
-[[Project Page](https://gewu-lab.github.io/robo-valuerl/)] [[GitHub](https://github.com/Open-X-Humanoid/Robo-ValueRL)] [[Paper](#)]
+[[Project Page](https://gewu-lab.github.io/robo-valuerl/)] [[GitHub](https://github.com/Open-X-Humanoid/Robo-ValueRL)] [[Dataset](https://huggingface.co/datasets/X-Humanoid/Robo-ValueRL)] [[Paper](#)]
 
-This repository contains the **models and data** for **Robo-ValueRL: Reliable Value Estimation for Offline-to-Online Reinforcement Learning**.
+This repository contains the **model checkpoints** for **Robo-ValueRL: Reliable Value Estimation for Offline-to-Online Reinforcement Learning**.
 
 Robo-ValueRL studies how reliable value estimation can guide robotic policy learning from heterogeneous offline demonstrations and online rollout trajectories. The framework learns a history-conditioned value estimator, converts value differences into action-quality conditions for offline policy pretraining, and uses value-guided rollout filtering for stable online residual adaptation.
 
-## 🤖 Model Description
+## Model Description
 
 **Robo-ValueRL** is an offline-to-online robotic reinforcement learning framework centered on reliable value estimation. Instead of only reporting final task success, Robo-ValueRL explicitly diagnoses whether learned values capture global task progress and local action-level preference, then propagates these value signals into downstream policy learning.
 
@@ -21,25 +34,7 @@ The released model suite includes:
 3. **Online Residual Adaptation Module**  
    Learns lightweight corrections from value-filtered online rollouts while keeping the pretrained base policy frozen. This enables targeted failure recovery and self-correction without overwriting the offline prior.
 
-## 📦 Data Description
-
-The Robo-ValueRL dataset contains heterogeneous real-robot experience collected on two long-horizon manipulation tasks:
-
-- **Chip Insertion**: millimeter-level precision manipulation requiring PCB grasping, pose adjustment, chip grasping, and insertion.
-- **Block Disassembly**: generalizable object disassembly requiring robust grasping, separation, and classification behaviors.
-
-The dataset includes:
-
-- **240 hours** of offline demonstrations
-- **3,000+** online rollout trajectories
-- Multi-view robot observations
-- Language task instructions
-- Robot states and action chunks
-- Mixed-quality trajectories, including successful demonstrations, corrections, suboptimal behaviors, and failure cases
-- Value-derived action-quality labels / conditions for policy learning
-- Online rollout segments for residual adaptation
-
-## 🧱 Model Hierarchy
+## Model Hierarchy
 
 1. **Value Estimation**
    - Learns history-conditioned value functions from heterogeneous robot data.
@@ -53,7 +48,15 @@ The dataset includes:
    - Uses reliable value estimates to filter online rollout data.
    - Trains a lightweight residual adapter for targeted real-world improvement.
 
-## ✨ Key Features
+## Associated Dataset
+
+The models are trained and evaluated with the Robo-ValueRL dataset:
+
+[[Robo-ValueRL Dataset](https://huggingface.co/datasets/X-Humanoid/Robo-ValueRL)]
+
+The dataset contains heterogeneous real-robot demonstrations and online rollout trajectories for chip insertion and block disassembly.
+
+## Key Features
 
 * **Reliable Value Estimation**: Uses visual history to produce smoother progress estimates and sharper error responses.
 * **Value-Guided Data Utilization**: Prioritizes useful demonstrations and rollout segments from heterogeneous robot experience.
@@ -61,7 +64,7 @@ The dataset includes:
 * **Stable Offline-to-Online Improvement**: Improves real-world performance through residual adaptation while preserving the pretrained base policy.
 * **Real-Robot Evaluation**: Evaluated on precision chip insertion and generalizable block disassembly.
 
-## 📊 Highlights
+## Highlights
 
 - 86% final success on chip insertion
 - 84% final success on block disassembly
@@ -70,17 +73,20 @@ The dataset includes:
 - 240h offline demonstrations
 - 3,000+ online rollout trajectories
 
-## 🛠 Usage
+## Usage
 
-The released assets are organized for reproducing Robo-ValueRL's model and data pipeline.
+The released assets are organized for reproducing Robo-ValueRL's model pipeline.
 
 Please refer to the [GitHub repository](https://github.com/Open-X-Humanoid/Robo-ValueRL) for setup instructions, inference scripts, training code, and data-processing utilities.
 
-## 📝 Citation
-If you use the XR-1 model in your research, please cite our work:(Citation will be updated after the arXiv release.)
+## Citation
+
+If you use Robo-ValueRL in your research, please cite our work. Citation will be updated after the arXiv release.
+
+## License
 
-## 📜 License
 Please refer to the license file in the [GitHub repository](https://github.com/Open-X-Humanoid/Robo-ValueRL).
 
-## 📬 Contact
-For questions, please open an issue on our [GitHub repository](https://github.com/Open-X-Humanoid/Robo-ValueRL).
+## Contact
+
+For questions, please open an issue on our [GitHub repository](https://github.com/Open-X-Humanoid/Robo-ValueRL).