README.md

---
pretty_name: RealSource World
size_categories:
- 100B<n<1T
task_categories:
- robotics
language:
- en
tags:
- real-world
- dual-arm
- robotics manipulation
- humanoid robot
license: cc-by-nc-4.0
---

<div align="center">
 <video controls autoplay src="https://realmanrobot.github.io/real_source_dataset/assets/real_source_video-CQfv30ls.mp4"></video>
</div>

# RealSource World

RealSource World is a large-scale real-world robotics manipulation dataset collected using the RS-02 dual-arm humanoid robot. This dataset contains diverse long-horizon manipulation tasks performed in real-world environments, with detailed annotations for atomic skills and quality assessments.

# Key Features 

- **14+ million** frames of real-world dual-arm manipulation demonstrations.
- **11,428+** episodes across **36** distinct manipulation tasks.
- **57-dimensional** proprioceptive state space including joint positions, velocities, forces, torques, and end-effector poses.
- **Multi-camera** visual observations (head camera, left hand camera, right hand camera) at 720x1280 resolution, 30 FPS.
- **Fine-grained annotations** with atomic skill segmentation and quality assessments for each episode.
- **Diverse scenes** including kitchen, conference room, convenience store, and household environments.
- **Dual-arm coordination** tasks demonstrating complex bimanual manipulation skills.

# News
- **`[2025/12]`** RealSource World dataset fully uploaded to Hugging Face, containing 36 tasks with a total size of 549GB. [Download Link](https://huggingface.co/datasets/RealSourceData/RealSource-World)
- **`[2025/11]`** RealSource World released on Hugging Face. [Download Link](https://huggingface.co/datasets/RealSourceData/RealSource-World)

# Changelog
## Version History

### Version 1.1 (December 2025)
- **Complete Dataset Upload**
 - Fully uploaded all dataset files to Hugging Face
 - Total dataset size: 549GB
 - Total files: approximately 104,907 files
 - Contains 36 manipulation tasks

### Version 1.0 (November 2025)
- **Initial Release**
 - Released RealSource World dataset on Hugging Face
 - 36 manipulation tasks with 11,428 episodes
 - 14+ million frames of real-world dual-arm manipulation demonstrations
 - 57-dimensional proprioceptive state space
 - Multi-camera visual observations (head, left hand, right hand cameras)
 - Fine-grained annotations with atomic skill segmentation
 - Complete camera parameters (intrinsic and extrinsic) for all episodes
 - Quality assessments for each episode

# Table of Contents

- [Key Features](#key-features-)
- [News](#news-)
- [Changelog](#changelog-)
- [Get Started](#get-started-)
 - [Download the Dataset](#download-the-dataset)
 - [Dataset Structure](#dataset-structure)
 - [Understanding the Dataset Format](#understanding-the-dataset-format)
 - [Loading and Using the Dataset](#loading-and-using-the-dataset)
- [Data Format Details](#data-format-details)
 - [Proprioceptive State (57-dimensional)](#proprioceptive-state-57-dimensional)
 - [Action Space (17-dimensional)](#action-space-17-dimensional)
 - [Visual Observations](#visual-observations)
 - [Camera Parameters](#camera-parameters)
 - [Sub-task Annotations](#sub-task-annotations)
- [Dataset Statistics](#dataset-statistics)
- [Robot URDF Model](#robot-urdf-model)
- [License and Citation](#license-and-citation)

# Get Started 

## Dataset Access

The RealSource World dataset has been fully uploaded to Hugging Face and can be accessed via:
- **Hugging Face Repository**: [RealSourceData/RealSource-World](https://huggingface.co/datasets/RealSourceData/RealSource-World)
- **Dataset Size**: 549GB
- **File Format**: LeRobot v2.1 format
- **Data Organization**: Organized by tasks, each task contains data/, meta/, and videos/ directories

## Download the Dataset

To download the full dataset, you can use the following code. If you encounter any issues, please refer to the official Hugging Face documentation.

**Note**: Due to the large dataset size (549GB), it is recommended to use Git LFS for downloading, or use the Hugging Face Datasets library to load data on-demand.

```bash

# Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install

# When prompted for a password, use an access token with read permissions.

Generate one from your settings: https://huggingface.co/settings/tokens
git clone https://huggingface.co/datasets/RealSourceData/RealSource-World

# If you want to clone without large files - just their pointers
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/RealSourceData/RealSource-World
```

If you only want to download a specific task from the RealSource World dataset, such as `Arrange_the_cups`, follow these steps:

```bash

# Ensure Git LFS is installed (https://git-lfs.com)
git lfs install

# Initialize an empty Git repository
git init RealSource-World
cd RealSource-World

# Set the remote repository
git remote add origin https://huggingface.co/datasets/RealSourceData/RealSource-World

# Enable sparse-checkout
git sparse-checkout init

# Specify the folders and files you want to download
git sparse-checkout set Arrange_the_cups scripts

# Pull the data from the main branch
git pull origin main
```

## Dataset Structure

### Folder Hierarchy

```
RealSource-world/
├── Arrange_the_cups/

# Task name (36 tasks in total)
│ ├── data/
│ │ └── chunk-000/
│ │ ├── episode_000000.parquet
│ │ ├── episode_000001.parquet
│ │ └── ...

## 871 parquet files for this task
│ ├── meta/
│ │ ├── info.json

# Dataset metadata and feature definitions
│ │ ├── episodes.jsonl

# Episode-level metadata
│ │ ├── episodes_stats.jsonl

# Episode statistics
│ │ ├── tasks.jsonl

# Task descriptions
│ │ ├── sub_tasks.jsonl

# Fine-grained sub-task annotations
│ │ └── camera.json

# Camera parameters for all episodes
│ └── videos/
│ └── chunk-000/
│ ├── observation.images.head_camera/
│ │ ├── episode_000000.mp4
│ │ └── ...
│ ├── observation.images.left_hand_camera/
│ │ ├── episode_000000.mp4
│ │ └── ...
│ └── observation.images.right_hand_camera/
│ ├── episode_000000.mp4
│ └── ...
├── Arrange_the_items_on_the_conference_table/
│ └── ...
├── Clean_the_convenience_store/
│ └── ...
└── ...

## 36 tasks in total
```

## Understanding the Dataset Format

This dataset follows the **LeRobot v2.1** format. Each task directory contains:

- **`data/`**: Parquet files storing time-series data (proprioceptive states, actions, timestamps)
- **`meta/`**: JSON/JSONL files with metadata, episode information, and annotations
- **`videos/`**: MP4 video files from three camera perspectives

### Key Metadata Files

- **`meta/info.json`**: Contains dataset-level metadata including:
 - Total episodes, frames, videos
 - Feature definitions (action and observation shapes, names)
 - Video specifications (resolution, codec, FPS)
 - Robot type and codebase version

- **`meta/episodes.jsonl`**: One JSON object per line, each representing an episode with:
 - `episode_index`: Episode identifier
 - `length`: Number of frames in the episode
 - `tasks`: List of task descriptions
 - `videos`: Paths to video files for each camera

- **`meta/sub_tasks.jsonl`**: Fine-grained annotations for each episode, including:
 - `task_steps`: List of atomic skill segments with start/end frames
 - `success_rating`: Overall task success score (1-5)
 - `quality_assessments`: Detailed quality metrics (PASS/FAIL/VALID)
 - `notes`: Annotation metadata

- **`meta/camera.json`**: Camera intrinsic and extrinsic parameters for each episode

## Loading and Using the Dataset

This dataset is compatible with the [LeRobot library](https://github.com/huggingface/lerobot). Here's how to load and use it:

```python
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset

# Load a specific task
dataset_path = "RealSource-World/Arrange_the_cups"
repo_id = "RealSourceData/RealSource-World"

# Initialize the dataset
dataset = LeRobotDataset(dataset_path, repo_id=repo_id)

# Access episode data
episode_0 = dataset[0]

# First frame of first episode
episode_info = dataset.episode_data[0]

# Episode metadata

Iterate through episodes
for episode_idx in range(len(dataset.episode_data)):
 episode_length = dataset.episode_data[episode_idx]["length"]
 print(f"Episode {episode_idx} has {episode_length} frames")

# Visualize an episode
dataset.show_video(episode_idx=0, video_key="observation.images.head_camera")
```

# Data Format Details

## Proprioceptive State (57-dimensional)

The `observation.state` field contains comprehensive proprioceptive information:

| Index Range | Components | Description |
|------------|-----------|-------------|
| 0-15 | Joint positions | 7 joints × 2 arms + 2 grippers = 16 DOF |
| 16 | Lift position | Mobile base lift height |
| 17-22 | Left arm force/torque | 6D force (fx, fy, fz, mx, my, mz) |
| 23-28 | Right arm force/torque | 6D force (fx, fy, fz, mx, my, mz) |
| 29-35 | Left joint velocities | 7 joints = 7 DOF |
| 36-42 | Right joint velocities | 7 joints = 7 DOF |
| 43-49 | Left end-effector pose | Position (x, y, z) + Quaternion (qw, qx, qy, qz) |
| 50-56 | Right end-effector pose | Position (x, y, z) + Quaternion (qw, qx, qy, qz) |

### State Field Names

```python
[
 "LeftFollowerArm_Joint1.pos", ..., "LeftFollowerArm_Joint7.pos",
 "LeftGripper.pos",
 "RightFollowerArm_Joint1.pos", ..., "RightFollowerArm_Joint7.pos",
 "RightGripper.pos",
 "Lift.position",
 "LeftForce.fx", "LeftForce.fy", "LeftForce.fz",
 "LeftForce.mx", "LeftForce.my", "LeftForce.mz",
 "RightForce.fx", "RightForce.fy", "RightForce.fz",
 "RightForce.mx", "RightForce.my", "RightForce.mz",
 "LeftJoint_Vel1", ..., "LeftJoint_Vel7",
 "RightJoint_Vel1", ..., "RightJoint_Vel7",
 "LeftEnd_X", "LeftEnd_Y", "LeftEnd_Z",
 "LeftEnd_Qw", "LeftEnd_Qx", "LeftEnd_Qy", "LeftEnd_Qz",
 "RightEnd_X", "RightEnd_Y", "RightEnd_Z",
 "RightEnd_Qw", "RightEnd_Qx", "RightEnd_Qy", "RightEnd_Qz"
]
```

## Action Space (17-dimensional)

The `action` field contains commands sent to the robot:

| Components | Description |
|-----------|-------------|
| 0-6 | Left arm joint positions (7 DOF) |
| 7 | Left gripper position |
| 8-14 | Right arm joint positions (7 DOF) |
| 15 | Right gripper position |
| 16 | Lift command |

### Action Field Names

```python
[
 "LeftLeaderArm_Joint1.pos", ..., "LeftLeaderArm_Joint7.pos",
 "LeftGripper.pos",
 "RightLeaderArm_Joint1.pos", ..., "RightLeaderArm_Joint7.pos",
 "RightGripper.pos",
 "Lift.command"
]
```

## Visual Observations

Each episode includes synchronized video from three camera perspectives:

- **`observation.images.head_camera`**: Overhead/head-mounted view
- **`observation.images.left_hand_camera`**: Left end-effector mounted camera
- **`observation.images.right_hand_camera`**: Right end-effector mounted camera

**Video Specifications:**
- Resolution: 720 × 1280 pixels
- Frame rate: 30 FPS
- Codec: H.264
- Format: MP4

## Camera Parameters

Each episode has corresponding camera parameters stored in `meta/camera.json`, keyed by `episode_XXXXXX`. The camera parameters include intrinsic parameters (camera matrix and distortion coefficients) and extrinsic parameters (hand-eye calibration).

### File Structure

The `camera.json` file contains camera parameters for all episodes:

```json
{
 "episode_000000": {
 "camera_ids": {
 "head": "245022300889",
 "left_arm": "245022301980",
 "right_arm": "245022300408",
 "foot": ""
 },
 "camera_parameters": {
 "head": {
 "720P": {
 "MTX": [[648.57, 0, 645.54], [0, 647.80, 375.38], [0, 0, 1]],
 "DIST": [-0.0513, 0.0587, -0.0006, 0.00096, -0.0186]
 },
 "480P": { ... }
 },
 "left_arm": { ... },
 "right_arm": { ... }
 },
 "hand_eye": {
 "left_arm_in_eye": {
 "R": [[...], [...], [...]],
 "T": [x, y, z]
 },
 "right_arm_in_eye": { ... },
 "left_arm_to_eye": { ... },
 "right_arm_to_eye": { ... }
 }
 },
 "episode_000001": { ... }
}
```

### Camera Intrinsic Parameters

Each camera (head, left_arm, right_arm) has intrinsic parameters for two resolutions:

- **`MTX`**: 3×3 camera intrinsic matrix
 ```
 [fx 0 cx]
 [0 fy cy]
 [0 0 1]
 ```
 - `fx`, `fy`: Focal lengths in pixels
 - `cx`, `cy`: Principal point (optical center) in pixels

- **`DIST`**: 5-element distortion coefficients (k1, k2, p1, p2, k3)
 - Used for correcting radial and tangential distortion

**Available Resolutions:**
- `720P`: Parameters for 720p video (720 × 1280)
- `480P`: Parameters for 480p video (480 × 640)

### Hand-Eye Calibration (Extrinsic Parameters)

The `hand_eye` section contains transformations between the robot end-effectors and cameras:

- **`left_arm_in_eye`**: Transformation from left end-effector camera to left arm end-effector center
 - `R`: 3×3 rotation matrix
 - `T`: 3×1 translation vector [x, y, z] in meters
 - Represents the pose of the left wrist-mounted camera relative to the left arm end-effector center

- **`right_arm_in_eye`**: Transformation from right end-effector camera to right arm end-effector center
 - Represents the pose of the right wrist-mounted camera relative to the right arm end-effector center

- **`left_arm_to_eye`**: Transformation from head camera to left arm base coordinate frame
 - `R`: 3×3 rotation matrix
 - `T`: 3×1 translation vector [x, y, z] in meters
 - Represents the pose of the head camera relative to the left arm base frame

- **`right_arm_to_eye`**: Transformation from head camera to right arm base coordinate frame
 - Represents the pose of the head camera relative to the right arm base frame

These parameters enable coordinate transformations between:
- Robot end-effector poses and camera image coordinates
- 3D positions in robot space and pixel coordinates in images
- Multi-view geometric operations and calibration
- Wrist camera frames and end-effector centers
- Head camera frame and arm base frames

### Camera IDs

Each camera has a unique identifier:
- **`head`**: Head-mounted camera ID
- **`left_arm`**: Left end-effector camera ID
- **`right_arm`**: Right end-effector camera ID
- **`foot`**: Foot camera ID (if available)

## Sub-task Annotations

Each episode in `meta/sub_tasks.jsonl` contains detailed annotations:

```json
{
 "task": "Separate the two stacked cups in the dish and place them on the two sides of the dish.",
 "language": "zh",
 "task_index": 0,
 "episode_index": 0,
 "task_steps": [
 {
 "step_name": "Left arm picks up the stack of cups from the center of the plate",
 "start_frame": 100,
 "end_frame": 180,
 "description": "Left arm picks up the stack of cups from the center of the plate",
 "duration_frames": 80
 },
 ...
 ],
 "success_rating": 5,
 "notes": "annotation_date: 2025/11/13",
 "quality_assessments": {
 "overall_valid": "VALID",
 "movement_fluency": "PASS",
 "grasp_success": "PASS",
 "placement_quality": "PASS",
 ...
 },
 "total_frames": 946
}
```

### Quality Assessment Metrics

- **`overall_valid`**: Overall episode validity (VALID/INVALID)
- **`movement_fluency`**: Smoothness of robot movements (PASS/FAIL)
- **`grasp_success`**: Success of grasping actions (PASS/FAIL)
- **`placement_quality`**: Quality of object placement (PASS/FAIL)
- **`no_drop`**: No objects were dropped during the task (PASS/FAIL)
- **`grasp_collisions`**: No collisions during grasping (PASS/FAIL)
- **`arm_collisions`**: No arm collisions (PASS/FAIL)
- **`operation_completeness`**: Task completion status (PASS/FAIL)
- And more...

# Dataset Statistics

## Overall Statistics

- **Total Tasks**: 36
- **Total Dataset Size**: 549GB
- **Total Files**: approximately 104,907 files
- **Total Episodes**: 11,428
- **Total Frames**: 14,085,107
- **Total Videos**: 34,284 (3 cameras × 11,428 episodes)
- **Robot Type**: RS-02 (dual-arm humanoid robot)
- **Dataset Format**: LeRobot v2.1
- **Video Resolution**: 720 × 1280
- **Frame Rate**: 30 FPS

## Task Distribution

The dataset includes diverse manipulation tasks across multiple domains:

- **Kitchen Tasks**: Arranging cups, cooking rice, steaming, cleaning counters, making toast, preparing birthday cake, etc.
- **Organization Tasks**: Organizing magazines, tools, toys, glass tubes, pen holders, TV cabinets, etc.
- **Household Tasks**: Tiding up rooms, placing books, slippers, hanging clothes to dry, etc.
- **Convenience Store Tasks**: Cleaning store, organizing items, collecting mail, etc.
- **Industrial Tasks**: Moving parts between containers, organizing glass tubes, etc.
- **Other Tasks**: Cable plugging, replenishing tea bags, organizing repair tools, etc.

**Complete Task List (36 tasks):**
1. Arrange_the_cups
2. Arrange_the_items_on_the_conference_table
3. Cable_Plugging_able
4. Clean_the_convenience_store
5. Collect_the_mail
6. Cook_rice_using_an_electric_rice_cooker
7. Hang_out_the_clothes_to_dry
8. Make_toast
9. Making_steamed_potatoes
10. Move_industrial_parts_to_different_plastic_boxes
11. Organize_the_TV_cabinet
12. Organize_the_glass_tube_on_the_rack
13. Organize_the_magazines
14. Organize_the_pen_holder
15. Organize_the_repair_tools
16. Organize_the_toys
17. Pack_the_badminton_shuttlecock
18. Place_the_books
19. Place_the_hairdryer
20. Place_the_slippers
21. Prepare_the_birthday_cake
22. Prepare_the_bread
23. Put_the_milk_in_the_refrigerator
24. Refill_the_laundry_detergent
25. Replace_the_tissues_and_arrange_them
26. Replenish_tea_bags
27. Stack_the_cups
28. Steam_buns
29. Steaming_rice_in_a_rice_cooker
30. Take_down_the_book
31. Take_out_the_trash
32. Tidy_up_the_children's_room
33. Tidy_up_the_children_s_room
34. Tidy_up_the_conference_room_table
35. Tidy_up_the_cooking_counter
36. Tidy_up_the_kitchen_counter

# Robot URDF Model

The RealSource World dataset was collected using the **RS-02** dual-arm humanoid robot. For simulation, visualization, and research purposes, we provide the URDF (Unified Robot Description Format) model of the RS-02 robot.

## RS-02 Robot Specifications

- **Robot Type**: Dual-arm humanoid robot
- **Total Links**: 46 links
- **Total Joints**: 45 joints
- **Arms**: 2 × 7-DOF arms (left and right)
- **End-effectors**: Dual-arm grippers with 8 DOF each
- **Base**: Mobile platform with wheels and lift mechanism
- **Sensors**: Head camera, left/right hand cameras

## URDF Package Structure

The RS-02 URDF package includes:

```
RS-02/
├── urdf/
│   ├── RS-02.urdf          # Main URDF file (59KB)
│   └── RS-02.csv           # Joint configuration data
├── meshes/                 # 3D mesh models (46 STL files)
│   ├── base_link.STL
│   ├── L_Link_1-7.STL      # Left arm links
│   ├── R_Link_1-7.STL      # Right arm links
│   ├── ltool_*.STL         # Left gripper components
│   ├── rtool_*.STL         # Right gripper components
│   ├── head_*.STL          # Head components
│   └── camera_*.STL        # Camera mounts
├── config/
│   └── joint_names_RS-02.yaml  # Joint name configuration
├── launch/
│   ├── display.launch      # RViz visualization
│   └── gazebo.launch       # Gazebo simulation
└── package.xml             # ROS package metadata
```

## Using the URDF Model

### For ROS/ROS2 Users

The URDF model can be used with ROS tools:

**Visualization in RViz:**
```bash
roslaunch RS-02 display.launch
```

**Simulation in Gazebo:**
```bash
roslaunch RS-02 gazebo.launch
```

# License and 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/). Please consider citing our project if it helps your research.

```BibTeX
@misc{realsourceworld,
 title={RealSource World: A Large-Scale Real-World Dual-Arm Manipulation Dataset},
  author={RealSource},
 howpublished={\url{https://huggingface.co/datasets/RealSourceData/RealSource-World}},
 year={2025}
}
```