Datasets:

OpenGalaxea
/

Galaxea-Open-World-Dataset

license: cc-by-nc-sa-4.0
language:
  - en
  - zh
size_categories:
  - n>1T
tags:
  - robotics
  - real-world
  - dual-arm
  - whole body control
  - manipulation
  - lerobot
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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 (R1-Lite) for consistency.
Fine-grained subtask language annotations (bilingual Chinese/English).
Covers residential, kitchen, retail, and office settings.
Dataset in LeRobot v2.1 format.

Dataset Structure

The dataset is organized as 227 task-level tar.gz archives under the lerobot/ directory. Each archive contains a self-contained LeRobot dataset for one task:

lerobot/
├── Adjust_The_Air_Conditioner_Temperature_20250711_006.tar.gz
├── Arrange_Fruits_20250819_011.tar.gz
├── Clean_The_Mirror_20250714_006.tar.gz
├── ...
└── Wipe_The_Sewage_Stains_With_A_Ground_Cloth_20250801_012.tar.gz

After extracting a task archive, the directory follows the standard LeRobot v2.1 layout:

<task_name>/
├── meta/
│   ├── info.json              # Dataset metadata (features, fps, splits, etc.)
│   ├── episodes.jsonl         # Per-episode info (tasks, length, source file)
│   └── episodes_stats.jsonl   # Per-episode statistics
├── data/
│   └── chunk-000/
│       ├── episode_000000.parquet
│       ├── episode_000001.parquet
│       └── ...
└── videos/
    └── chunk-000/
        ├── observation.images.head_rgb/
        │   ├── episode_000000.mp4
        │   └── ...
        ├── observation.images.head_right_rgb/
        ├── observation.images.left_wrist_rgb/
        └── observation.images.right_wrist_rgb/

LeRobot Dataset Schema

A detailed schema is available in lerobot_info.json. Below is a summary of all features:

Observations

Feature	Dtype	Shape	Description
`observation.images.head_rgb`	video	(720, 1280, 3)	Head camera RGB
`observation.images.head_right_rgb`	video	(720, 1280, 3)	Head right camera RGB
`observation.images.left_wrist_rgb`	video	(720, 1280, 3)	Left wrist camera RGB
`observation.images.right_wrist_rgb`	video	(720, 1280, 3)	Right wrist camera RGB
`observation.state.left_arm`	float64	(6,)	Left arm joint positions
`observation.state.left_arm.velocities`	float64	(6,)	Left arm joint velocities
`observation.state.right_arm`	float64	(6,)	Right arm joint positions
`observation.state.right_arm.velocities`	float64	(6,)	Right arm joint velocities
`observation.state.torso`	float64	(4,)	Torso joint positions
`observation.state.torso.velocities`	float64	(4,)	Torso joint velocities
`observation.state.chassis`	float64	(3,)	Chassis positions
`observation.state.chassis.velocities`	float64	(3,)	Chassis velocities
`observation.state.chassis.imu`	float64	(10,)	Chassis IMU (orientation, angular velocity, linear acceleration)
`observation.state.left_gripper`	float64	(1,)	Left gripper state (0-close, 100-open)
`observation.state.right_gripper`	float64	(1,)	Right gripper state (0-close, 100-open)
`observation.state.left_ee_pose`	float64	(7,)	Left end-effector pose (position + quaternion)
`observation.state.right_ee_pose`	float64	(7,)	Right end-effector pose (position + quaternion)

Actions

Feature	Dtype	Shape	Description
`action.left_arm`	float64	(6,)	Target left arm joint positions
`action.right_arm`	float64	(6,)	Target right arm joint positions
`action.left_gripper`	float64	(1,)	Target left gripper position
`action.right_gripper`	float64	(1,)	Target right gripper position
`action.chassis.velocities`	float64	(6,)	Target chassis twist (linear + angular)
`action.torso.velocities`	float64	(6,)	Target torso twist (linear + angular)

Metadata Columns

Feature	Dtype	Description
`timestamp`	float32	Timestamp within episode
`frame_index`	int64	Frame index within episode
`episode_index`	int64	Episode index
`index`	int64	Global frame index
`task_index`	int64	Subtask annotation index
`coarse_task_index`	int64	Coarse-level task index
`quality_index`	int64	Quality annotation index
`coarse_quality_index`	int64	Coarse-level quality index

All video streams are encoded with AV1 codec at 15 fps.

Quick Start

Below is an example of how to download and load a single task from the dataset using the Hugging Face Hub and LeRobot:

import tarfile
from pathlib import Path
from huggingface_hub import hf_hub_download
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
import torch

# 1. Download a single task archive
task_name = "Clean_The_Mirror_20250714_006"
tar_path = hf_hub_download(
    repo_id="OpenGalaxea/Galaxea-Open-World-Dataset",
    filename=f"lerobot/{task_name}.tar.gz",
    repo_type="dataset",
)

# 2. Extract it
extract_dir = Path("./galaxea_data")
extract_dir.mkdir(exist_ok=True)
with tarfile.open(tar_path, "r:gz") as tar:
    tar.extractall(path=extract_dir)

# 3. Load with LeRobot
dataset = LeRobotDataset.from_local(str(extract_dir / task_name / task_name))

print(f"Number of episodes: {dataset.num_episodes}")
print(f"Number of frames:   {dataset.num_frames}")
print(f"FPS:                {dataset.fps}")

# 4. Access a single frame
frame = dataset[0]
print(f"\nFrame keys: {list(frame.keys())}")
print(f"Left arm state shape:  {frame['observation.state.left_arm'].shape}")
print(f"Left arm action shape: {frame['action.left_arm'].shape}")

# 5. Iterate over an episode
from torch.utils.data import DataLoader

dataloader = DataLoader(dataset, batch_size=32, shuffle=False)
for batch in dataloader:
    print(f"Batch observation.state.left_arm shape: {batch['observation.state.left_arm'].shape}")
    break

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}
}