| --- |
| tags: |
| - Embodied-AI |
| - Robotics |
| - Offline-to-Online-RL |
| - Value-Function |
| - VLA-Model |
| - Robot-Foundation-Model |
| - Vision-Language-Action |
| - Robot-Learning |
| - Imitation-Learning |
| --- |
| |
| # Robo-ValueRL |
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| [[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](#)] |
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| This repository contains the **model checkpoints** for **Robo-ValueRL: Reliable Value Estimation for Offline-to-Online Reinforcement Learning**. |
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| 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. |
|
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| ## Model Description |
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| **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. |
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| The released model suite includes: |
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| 1. **History-Conditioned Value Estimator** |
| Predicts normalized task progress from multi-view robot observations, language instructions, and visual history. The temporal context helps reduce ambiguity caused by occlusions, repeated motions, and visually similar task stages. |
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| 2. **Quality-Conditioned VLA Policy** |
| Uses value differences to derive action-quality conditions. These conditions guide a Vision-Language-Action policy during offline pretraining, allowing the policy to prioritize useful behaviors from mixed-quality demonstrations. |
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| 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. |
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| ## Model Hierarchy |
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| 1. **Value Estimation** |
| - Learns history-conditioned value functions from heterogeneous robot data. |
| - Evaluates value reliability with global-progress and local-preference metrics. |
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| 2. **Offline Policy Pretraining** |
| - Converts value differences into action-quality conditions. |
| - Trains a quality-conditioned VLA policy on mixed-quality demonstrations. |
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| 3. **Online Policy Improvement** |
| - Uses reliable value estimates to filter online rollout data. |
| - Trains a lightweight residual adapter for targeted real-world improvement. |
|
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| ## Associated Dataset |
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| The models are trained and evaluated with the Robo-ValueRL dataset: |
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| [[Robo-ValueRL Dataset](https://huggingface.co/datasets/X-Humanoid/Robo-ValueRL)] |
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| The dataset contains heterogeneous real-robot demonstrations and online rollout trajectories for chip insertion and block disassembly. |
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| ## Key Features |
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| * **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. |
| * **Quality-Conditioned Policy Learning**: Conditions the VLA policy on value-derived action quality. |
| * **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. |
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| ## Highlights |
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| - 86% final success on chip insertion |
| - 84% final success on block disassembly |
| - +26% offline gain on chip insertion |
| - +34% offline gain on block disassembly |
| - 240h offline demonstrations |
| - 3,000+ online rollout trajectories |
|
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| ## Usage |
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| The released assets are organized for reproducing Robo-ValueRL's model pipeline. |
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| 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. |
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| ## Citation |
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| If you use Robo-ValueRL in your research, please cite our work. Citation will be updated after the arXiv release. |
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| ## License |
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| Please refer to the license file in the [GitHub repository](https://github.com/Open-X-Humanoid/Robo-ValueRL). |
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| ## Contact |
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| For questions, please open an issue on our [GitHub repository](https://github.com/Open-X-Humanoid/Robo-ValueRL). |
