Update README.md

README.md

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----
-license: apache-2.0
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+---
+license: apache-2.0
+tags:
+- robotics
+- vla-model
+- embodied-ai
+- task-generation
+- humanoid-robots
+- lerobot
+- pytorch
+- agentic-framework
+datasets:
+- robogene
+---
+
+
+# Dataset Card for RoboGene-Dataset
+
+[![Paper](https://arxiv.org/abs/2602.16444)]
+[![Dataset](https://huggingface.co/datasets/X-Humanoid/RoboGene)]
+[![License](https://img.shields.io/badge/License-Apache--2.0-blue.svg)](LICENSE)
+**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.
+
+
+
+## Dataset Details
+
+### Dataset Description
+
+RoboGene overcomes the limitations of traditional human teleoperation for data collection. By leveraging foundation models (e.g., GPT-4o) as generators within the **RoboGene Agent Loop**—which integrates retrieval, generation, and self-reflection—the framework ensures that every generated instruction is not only semantically diverse but also physically executable in structured environments.
+
+- **Curated by:** Beijing Innovation Center of Humanoid Robotics, Peking University, Beihang University, Beijing Institute of Technology.
+- **Shared by:** RoboGene Project Team
+- **Language(s) (NLP):** Chinese (Task Instructions)
+- **License:** Apache-2.0
+
+### Dataset Sources
+
+- **Demo:** (https://robogene-boost-vla.github.io/)
+- **Paper:** *RoboGene: Boosting VLA Pre-training via Diversity-Driven Agentic Framework for Real-World Task Generation* 
+- **Framework Compatibility:** Native support for the [LeRobot](https://github.com/huggingface/lerobot) framework.
+
+## Uses
+
+### Direct Use
+
+This dataset serves as a **pre-training foundation** for VLA models:
+- **Massive-Scale Pre-training:** Enhances model robustness against novel objects, lighting variations, and background clutter.
+- **Zero/Few-shot Adaptation:** Experiments show that models pre-trained on RoboGene require only 50 human demonstrations to quickly adapt to complex downstream tasks.
+- **Dual-arm Coordination Research:** Contains high-quality trajectories for sophisticated dual-arm manipulation.
+
+### Out-of-Scope Use
+
+- Deploying pre-trained policies directly to heavy industrial equipment without safety validation or simulation-based verification is strictly prohibited.
+
+## Dataset Structure
+
+The dataset is organized by scenes with a balanced distribution. Each episode includes:
+- **Observations:** Multi-view RGB images (front-view, wrist-view) and proprioceptive states.
+- **Actions:** High-frequency control sequences for joints or end-effectors.
+- **Instructions:** Verified, high-quality natural language task descriptions.
+
+### Task Highlights
+- **100+ Diverse Skills:** Ranging from basic pick-and-place to complex multi-stage assembly and tool usage.
+- **Rich Scenarios:** Comprehensive coverage of Kitchen, Laboratory, Medical, Office, Retail, Education, Industrial, and Daily Life environments.
+- **Complex Object Spectrum:** Includes high-reflectivity materials (e.g., glass), deformable/flexible objects (e.g., cloth, ropes), and articulated/hinged mechanisms (e.g., doors, drawers, cabinets).
+
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+While human-curated data is physically valid, it often lacks structural diversity. Conversely, traditional LLM-generated tasks frequently fail to align with physical constraints. RoboGene bridges this gap by embedding physical grounding into an automated generation loop, resulting in pre-training performance that significantly outperforms both purely human-curated and baseline foundation-model datasets.
+
+
+## Bias, Risks, and Limitations
+
+- **Sim-to-Real Gap:** Although verified for physical grounding, minor sim-to-real discrepancies may still occur in extremely high-precision contact mechanics tasks.
+- **Domain Scope:** While covering 8+ major scenarios, the dataset may not account for highly specialized or hazardous industrial environments not represented in the training distribution.
+
+### Performance Benchmarks
+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.
+## Citation
+
+If you use this dataset in your research, please cite:
+
+**BibTeX:**
+
+```latex
+@article{zhang2026robogene,
+  title={RoboGene: Boosting VLA Pre-training via Diversity-Driven Agentic Framework for Real-World Task Generation},
+  author={Zhang, Yixue and Wu, Kun and Gao, Zhi and Zhao, Zhen and Ren, Pei and Xu, Zhiyuan and Liao, Fei and Wang, Xinhua and Fan, Shichao and Wu, Di and others},
+  journal={arXiv preprint arXiv:2602.16444},
+  year={2026}
+}