Update README.md

README.md

@@ -1,3 +1,138 @@
 ---
 license: cc-by-nc-sa-4.0
+language:
+- en
+- zh
+size_categories:
+- n>1T
+tags:
+- real-world
+- dual-arm
+- whole body control
+- manipulation
 ---
+
+# 🚀 Galaxea Open-World Dataset
+[![Project Page](https://img.shields.io/badge/Project%20Page-000000?style=for-the-badge&logo=github)](https://opengalaxea.github.io/G0/)
+[![Paper](https://img.shields.io/badge/Paper-8A2BE2?style=for-the-badge&logo=arxiv)](https://github.com/OpenGalaxea/G0/blob/main/Galaxea_G0_report.pdf)
+[![Videos](https://img.shields.io/badge/Videos-FF0000?style=for-the-badge&logo=youtube)](https://opengalaxea.github.io/G0/)
+[![Visualizer](https://img.shields.io/badge/Visualizer-FF8C00?style=for-the-badge&logo=airplayvideo)](https://opengalaxea.github.io/G0/visualizer/index.html)
+[![Modelscope](https://img.shields.io/badge/Modelscope-1890FF?style=for-the-badge&logo=alibabacloud)](https://www.modelscope.cn/organization/Galaxea)
+
+## Key features
+- **500+ hours** of real-world mobile manipulation data.
+- All data collected using **one uniform robotic embodiment** for consistency.
+- Fine-grained **subtask language annotations**.
+- Covers **residential**, **kitchen**, **retail**, and **office** settings.
+- Dataset in **RLDS** format.
+
+## Dataset Structure
+**For convenience, we divided the 500 hours of data into four equal parts by time. We also provide a small sample dataset for quick start.**
+```
+rlds
+├── part1_r1_lite
+│   ├── 1.0.0
+│   │   ├── dataset_info.json
+│   │   ├── features.json
+│   │   ├── merge_dataset_large_r1_lite-train.tfrecord-00000-of-02048
+│   │   ├── ...
+│   │   ├── merge_dataset_large_r1_lite-train.tfrecord-02047-of-02048
+├── part2_r1_lite
+├── part3_r1_lite
+├── part4_r1_lite
+├── sample
+│   ├── 1.0.0
+│   │   ├── merge_dataset_large_r1_lite-train.tfrecord-00000-of-01024
+│   │   ├── ...
+│   │   ├── merge_dataset_large_r1_lite-train.tfrecord-01023-of-01024
+```
+
+## Dataset Schema
+
+```
+OpenGalaxeaDataset = {
+    "episode_metadata": {
+        "file_path": tf.Text,  # path to the original data file
+    },
+    "steps": {
+        "is_first": tf.Scalar(dtype=bool),  # true on first step of the episode
+        "is_last": tf.Scalar(dtype=bool),  # true on last step of the episode
+
+        "language_instruction": tf.Text,  # language instruction, format: "high level"@"low level chinese"@"low level english"
+        "observation": {
+            "base_velocity": tf.Tensor(3, dtype=float32),   # robot base velocity
+            "gripper_state_left": tf.Tensor(1, dtype=float32),  # left gripper state, 0-close and 100-open
+            "gripper_state_right": tf.Tensor(1, dtype=float32), # right gripper state, 0-close and 100-open
+            "depth_camera_wrist_left": tf.Tensor(224, 224, 1, dtype=uint16),  # wrist camera depth left viewpoint, unit: mm
+            "depth_camera_wrist_right": tf.Tensor(224, 224, 1, dtype=uint16),  # wrist camera depth right viewpoint, unit: mm
+            "image_camera_head": tf.Tensor(224, 224, 3, dtype=uint8), # head camera RGB viewpoint
+            "image_camera_wrist_left": tf.Tensor(224, 224, 3, dtype=uint8), # wrist camera RGB left viewpoint
+            "image_camera_wrist_right": tf.Tensor(224, 224, 3, dtype=uint8), # wrist camera RGB right viewpoint
+            "joint_position_arm_left": tf.Tensor(6, dtype=float32), # joint positions of the left arm
+            "joint_position_arm_right": tf.Tensor(6, dtype=float32), # joint positions of the right arm
+            "joint_position_torso": tf.Tensor(4, dtype=float32), # joint positions of the torso
+            "joint_velocity_arm_left": tf.Tensor(6, dtype=float32), # joint velocities of the left arm
+            "joint_velocity_arm_right": tf.Tensor(6, dtype=float32), # joint velocities of the right arm
+            "last_action": tf.Tensor(26, dtype=float32), # history of the last action
+        },
+        # action dimensions:
+        # 26 = 6 (left arm) + 1 (left gripper) + 6 (right arm) + 1 (right gripper) + 6 (torso) + 6 (base)
+        "action": tf.Tensor(26, dtype=float32),  # robot action, consists of [6x joint velocities, 1x gripper position]
+        "segment_idx": tf.Scalar(dtype=int32),  # index of the segment in the episode
+        "variant_idx": tf.Scalar(dtype=int32), 
+    },
+}
+```
+
+## Example
+
+We provide an example script to load our RLDS dataset and transform some episodes into mp4 video format (head camera).
+
+```python
+import tensorflow_datasets as tfds
+import tyro
+import os
+import imageio
+from tqdm import tqdm
+
+def main(
+    dataset_name: str, 
+    data_dir: str, 
+    output_dir: str = "extracted_videos",
+    num_trajs: int = 10
+):
+    ds = tfds.load(dataset_name, split='train', data_dir=data_dir)
+    print(f"Successfully loaded dataset: {dataset_name}")
+
+    os.makedirs(output_dir, exist_ok=True)
+    print(f"Videos will be saved to: {output_dir}")
+
+    for i, episode in enumerate(tqdm(ds.take(num_trajs), total=num_trajs, desc="Exporting videos")):
+        head_frames = []
+        
+        for step in episode['steps']:
+            head_rgb_image = step['observation']['image_camera_head'].numpy()
+            head_frames.append(head_rgb_image)
+            instruction = step['language_instruction'].numpy().decode('utf-8')
+
+        video_path = os.path.join(output_dir, f"traj_{i}_head_rgb.mp4")
+        try:
+            imageio.mimsave(video_path, head_frames, fps=15)
+            print(f"Saved video for episode {i} to {video_path} with instruction: '{instruction}'")
+        except Exception as e:
+            print(f"Error saving video for episode {i}: {e}")
+
+if __name__ == '__main__':
+    tyro.cli(main)
+```
+## 📜 Citation
+
+All the data and code within this repo are under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). If you use our dataset or models, please cite:
+
+```bibtex
+@article{galaxea2025,
+  title={Galaxea G0: Open-World Dataset and Dual-System VLA Model},
+  author={Galaxea Team},
+  journal={arXiv preprint arXiv:XXXX.XXXXX},
+  year={2025}
+}