--- pretty_name: RealSource World size_categories: - 100B # 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} } ```