RealSource-World

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

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
[2025/11] RealSource World released on Hugging Face. Download Link

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

Key Features
News
Changelog
Get Started
Download the Dataset
Dataset Structure
Understanding the Dataset Format
Loading and Using the Dataset
Data Format Details
Proprioceptive State (57-dimensional)
Action Space (17-dimensional)
Visual Observations
Camera Parameters
Sub-task Annotations
Dataset Statistics
Robot URDF Model
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
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.


# 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:


# 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. Here's how to load and use it:

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

[
 "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

[
 "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:

{
 "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:

{
 "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):

Arrange_the_cups
Arrange_the_items_on_the_conference_table
Cable_Plugging_able
Clean_the_convenience_store
Collect_the_mail
Cook_rice_using_an_electric_rice_cooker
Hang_out_the_clothes_to_dry
Make_toast
Making_steamed_potatoes
Move_industrial_parts_to_different_plastic_boxes
Organize_the_TV_cabinet
Organize_the_glass_tube_on_the_rack
Organize_the_magazines
Organize_the_pen_holder
Organize_the_repair_tools
Organize_the_toys
Pack_the_badminton_shuttlecock
Place_the_books
Place_the_hairdryer
Place_the_slippers
Prepare_the_birthday_cake
Prepare_the_bread
Put_the_milk_in_the_refrigerator
Refill_the_laundry_detergent
Replace_the_tissues_and_arrange_them
Replenish_tea_bags
Stack_the_cups
Steam_buns
Steaming_rice_in_a_rice_cooker
Take_down_the_book
Take_out_the_trash
Tidy_up_the_children's_room
Tidy_up_the_children_s_room
Tidy_up_the_conference_room_table
Tidy_up_the_cooking_counter
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:

roslaunch RS-02 display.launch

Simulation in Gazebo:

roslaunch RS-02 gazebo.launch

License and Citation

All the data and code within this repo are under CC BY-NC-SA 4.0. Please consider citing our project if it helps your research.

@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}
}