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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). |