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@@ -25,6 +25,7 @@ datasets:
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  <img src="teaser.png" width="800">
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  </p>
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  <p align="center"><em>Figure 1: Overview of the RoboGene dataset.</em></p>
 
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  **RoboGene** is a large-scale robotic task dataset generated by a **diversity-driven agentic framework**. Specifically designed for **Vision-Language-Action (VLA)** model pre-training, it addresses the critical bottlenecks of limited scene variety and insufficient physical grounding in existing datasets, significantly enhancing the generalization capabilities of humanoid robots in complex real-world tasks.
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  ### Performance Benchmarks
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  Empirical results demonstrate that the RoboGene pre-training policy significantly outperforms human-curated baselines across five unseen dual-arm tasks. Most notably, in the complex DFR-BuildBlocks scenario, RoboGene achieves a success rate five times higher than that of existing foundation models (25% vs. 5%). Furthermore, our approach exhibits superior generalization capabilities, maintaining robust performance in novel environments where baseline policies frequently fail to adapt.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Citation
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  If you use this dataset in your research, please cite:
 
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  <img src="teaser.png" width="800">
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  </p>
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  <p align="center"><em>Figure 1: Overview of the RoboGene dataset.</em></p>
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+
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  **RoboGene** is a large-scale robotic task dataset generated by a **diversity-driven agentic framework**. Specifically designed for **Vision-Language-Action (VLA)** model pre-training, it addresses the critical bottlenecks of limited scene variety and insufficient physical grounding in existing datasets, significantly enhancing the generalization capabilities of humanoid robots in complex real-world tasks.
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  ### Performance Benchmarks
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  Empirical results demonstrate that the RoboGene pre-training policy significantly outperforms human-curated baselines across five unseen dual-arm tasks. Most notably, in the complex DFR-BuildBlocks scenario, RoboGene achieves a success rate five times higher than that of existing foundation models (25% vs. 5%). Furthermore, our approach exhibits superior generalization capabilities, maintaining robust performance in novel environments where baseline policies frequently fail to adapt.
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+ ## structure of the dataset
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+
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+ Each task follows the LeRobot format:
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+ ```
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+ task_name/
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+ ├── data/
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+ │ └── chunk-000/
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+ │ ├── episode_000000.parquet
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+ │ ├── episode_000001.parquet
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+ │ └── ...
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+ ├── meta/
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+ │ ├── info.json #
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+ │ ├── tasks.jsonl # task description
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+ │ ├── episodes.jsonl #
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+ │ └── episodes_stats.jsonl
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+ └── videos/
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+ └── chunk-000/
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+ ├── observation.rgb_images.camera_front/
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+ ├── observation.rgb_images.camera_left/
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+ ├── observation.rgb_images.camera_right/
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+ ├── observation.rgb_images.camera_top/
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+ ├── observation.rgb_images.camera_wrist_left/
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+ └── observation.rgb_images.camera_wrist_right/
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+ ```
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+ ### camera configuration(six RGB cameras)
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+ - `camera_front`
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+ - `camera_left`
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+ - `camera_right`
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+ - `camera_top`
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+ - `camera_wrist_left`
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+ - `camera_wrist_right`
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  ## Citation
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  If you use this dataset in your research, please cite: