Update README.md

README.md

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 ---
 license: apache-2.0
+task_categories:
+- robotics
+- video-classification
+- image-to-text
+tags:
+- vla-arena
+- robotics
+- multimodal
+- imitation-learning
+- vision-language-action
+- lerobot
+- openpi
+size_categories:
+- 10K<n<100K
 ---
 
+# VLA-Arena Dataset (L0 - Large Variant)
+
+## About VLA-Arena
+
+VLA-Arena is an open-source benchmark designed for the systematic evaluation of Vision-Language-Action (VLA) models. It provides a complete and unified toolchain covering scene modeling, demonstration collection, model training, and evaluation. Featuring 150+ tasks across 11 specialized suites, VLA-Arena assesses models through hierarchical difficulty levels (L0-L2) to ensure comprehensive metrics for safety, generalization, and efficiency.
+
+**Key Evaluation Domains**
+VLA-Arena focuses on four critical dimensions to ensure robotic agents can operate effectively in the real world:
+* **Safety**: Evaluate the ability to operate reliably in the physical world while avoiding static/dynamic obstacles and hazards.
+* **Distractor**: Assess performance stability when facing environmental unpredictability and visual clutter.
+* **Extrapolation**: Test the ability to generalize learned knowledge to novel situations, unseen objects, and new workflows.
+* **Long Horizon**: Challenge agents to combine long sequences of actions to achieve complex, multi-step goals.
+
+**Highlights**
+* **End-to-End Toolchain**: From scene construction to final evaluation metrics.
+* **Systematic Difficulty Scaling**: Tasks range from basic object manipulation (L0) to complex, constraint-heavy scenarios (L2).
+* **Flexible Customization**: Powered by CBDDL (Constrained Behavior Domain Definition Language) for easy task definition.
+
+**Resources**
+* **Project Homepage**: [VLA-Arena Website](https://vla-arena.github.io)
+* **GitHub Repository**: [PKU-Alignment/VLA-Arena](https://github.com/PKU-Alignment/VLA-Arena)
+* **Documentation**: [Read the Docs](https://github.com/PKU-Alignment/VLA-Arena/tree/main/docs)
+
+---
+
+## Dataset Description
+
+This dataset is the **Level 0 (L0) - Large (L)** variant of the VLA-Arena benchmark data. It contains a balanced set of human demonstrations suitable for standard training scenarios.
+
+* **Tasks Covered**: 60 distinct tasks at Difficulty Level 0.
+* **Total Trajectories**: 3,000 (50 trajectories per task).
+* **Task Suites**: Covers Safety, Distractor, Extrapolation, and Long Horizon domains.
+
+### Format and Compatibility
+This dataset is strictly formatted according to the **RLDS** format.
+
+The data structure includes standardized features for:
+* **Observation**: High-resolution RGB images (256x256) and robot state vectors.
+* **Action**: 7-DoF continuous control signals (End-effector pose + Gripper).
+* **Language**: Natural language task instructions.
+
+## Dataset Construction and Preprocessing
+
+To ensure high data quality and fair comparison, the dataset underwent several rigorous construction and quality control steps:
+
+**1. High-Resolution Regeneration**
+The demonstrations were re-rendered at a higher resolution of 256 x 256. Simple upscaling of the original 128 x 128 benchmark images resulted in poor visual fidelity. We re-executed the recorded action trajectories in the simulator to capture superior visual observations suitable for modern VLA backbones.
+
+**2. Camera Selection and Rotation**
+* **Viewpoint**: Only the static third-person camera images are utilized. Wrist camera images were discarded to ensure fair comparison across baselines.
+* **Rotation**: All third-person camera images were rotated by 180 degrees at both train and test time to correct for the visual inversion observed in the simulation environment.
+
+**3. Success Filtering**
+All demonstrations were replayed in the simulation environments. Any trajectory that failed to meet the task's success criteria during replay was filtered out.
+
+**4. Action Filtering (Iterative Optimization)**
+Standard data cleaning often involves filtering out all no-operation (no-op) actions. However, we found that completely removing no-ops significantly decreased the trajectory success rate upon playback in the VLA-Arena setup. To address this, we adopted an iterative optimization strategy:
+* Instead of removing all no-ops, we sequentially attempted to preserve N no-operation actions (N = 4, 8, 12, 16), specifically around critical state transition points (e.g., gripper closure and opening).
+* Only trajectories that remained successful during validation playback were retained.
+
+## Evaluation & Usage
+
+This dataset is designed to be used within the VLA-Arena benchmark ecosystem. It allows for the training of models that are subsequently tested across 11 specialized suites with difficulty levels ranging from L0 (Basic) to L2 (Advanced).
+
+For detailed evaluation instructions, metrics, and scripts, please refer to the VLA-Arena repository.
+<!-- 
+## Citation
+
+If you use this dataset or the VLA-Arena benchmark in your research, please cite:
+
+```bibtex
+@misc{vla-arena2025,
+  title={VLA-Arena: A Comprehensive Benchmark for Vision-Language-Action Models},
+  author={Jiahao Li, Borong Zhang, Jiachen Shen, Jiaming Ji, and Yaodong Yang},
+  journal={GitHub repository},
+  year={2025}
+} -->