Create README.md (#2)

- Create README.md (011d89d30f14157c31f242034617df90491f0f01)


Co-authored-by: Wenbo Yu <universalmariner@users.noreply.huggingface.co>

README.md

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+---
+tags:
+- Embodied-AI
+- Robotics
+- Offline-to-Online-RL
+- Robot-Dataset
+- VLA-Model
+- Vision-Language-Action
+- Robot-Learning
+- Imitation-Learning
+- Real-Robot-Data
+---
+
+# Robo-ValueRL Dataset
+
+[[Project Page](https://gewu-lab.github.io/robo-valuerl/)] [[GitHub](https://github.com/Open-X-Humanoid/Robo-ValueRL)] [[Model](https://huggingface.co/X-Humanoid/Robo-ValueRL)] [[Paper](#)]
+
+This repository contains the **dataset** for **Robo-ValueRL: Reliable Value Estimation for Offline-to-Online Reinforcement Learning**.
+
+The Robo-ValueRL dataset provides heterogeneous real-robot experience for studying reliable value estimation, value-guided offline policy pretraining, and online residual adaptation.
+
+## Dataset Description
+
+The Robo-ValueRL dataset contains real-robot trajectories 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
+
+## Associated Model
+
+The dataset is released together with the Robo-ValueRL model suite:
+
+[[Robo-ValueRL Model](https://huggingface.co/X-Humanoid/Robo-ValueRL)]
+
+The associated models include a history-conditioned value estimator, a quality-conditioned VLA policy, and an online residual adaptation module.
+
+## Data Usage
+
+The dataset is designed for:
+
+1. Training and evaluating history-conditioned value estimators.
+2. Studying value reliability under heterogeneous robotic data.
+3. Deriving action-quality conditions from value differences.
+4. Training quality-conditioned VLA policies.
+5. Evaluating value-guided offline-to-online reinforcement learning.
+6. Training online residual adapters from value-filtered rollout segments.
+
+## Task Setup
+
+### Chip Insertion
+
+A precision manipulation task where the robot must grasp a PCB, adjust it to a feasible insertion pose, grasp a chip, and insert it into millimeter-scale clearance.
+
+### Block Disassembly
+
+A generalizable manipulation task where the robot must grasp, separate, and classify block components under varied configurations.
+
+## Key Features
+
+* **Heterogeneous Robot Experience**: Includes successful, suboptimal, corrective, and failed trajectories.
+* **Offline and Online Data**: Supports both offline pretraining and online improvement studies.
+* **Value-Oriented Labels**: Provides value-derived action-quality conditions for policy learning.
+* **Real-Robot Evaluation**: Collected from physical robot manipulation tasks rather than simulation-only benchmarks.
+* **Offline-to-Online Pipeline Support**: Designed to connect value estimation, policy pretraining, and residual adaptation.
+
+## Highlights
+
+- 240h offline demonstrations
+- 3,000+ online rollout trajectories
+- Two real-robot manipulation tasks
+- Multi-view visual observations
+- Language-conditioned task instructions
+- Action-quality labels / conditions derived from reliable value estimation
+
+## Recommended Use
+
+This dataset can be used to reproduce the Robo-ValueRL pipeline or to study new methods for:
+
+- value estimation in robotic manipulation
+- data filtering from mixed-quality demonstrations
+- quality-conditioned VLA policy learning
+- offline-to-online reinforcement learning
+- stable online adaptation from real-world rollouts
+
+Please refer to the [GitHub repository](https://github.com/Open-X-Humanoid/Robo-ValueRL) for data loading, preprocessing, and training scripts.
+
+## Citation
+
+If you use the Robo-ValueRL dataset in your research, please cite our work. Citation will be updated after the arXiv release.
+
+## 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).