metadata
license: cc-by-nc-sa-4.0
language:
- en
- zh
size_categories:
- n>1T
tags:
- robotics
- real-world
- dual-arm
- whole body control
- manipulation
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🚀 Galaxea Open-World Dataset
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 and LeRobot 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
RLDS 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),
"segment_idx": tf.Scalar(dtype=int32), # index of the segment in the episode
"variant_idx": tf.Scalar(dtype=int32),
},
}
Lerobot Dataset Schema
A detailed lerobot format Galaxea Open-World Dataset schema can be seen at lerobot_info.json.
RLDS Example
We provide an example script to load our RLDS dataset and transform some episodes into mp4 video format (head camera).
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. If you use our dataset or models, please cite:
@article{galaxea2025,
title={Galaxea G0: Open-World Dataset and Dual-System VLA Model},
author={Galaxea Team},
journal={arXiv preprint arXiv:2509.00576},
year={2025}
}