--- license: cc-by-sa-4.0 task_categories: - robotics - reinforcement-learning language: - en - zh tags: - agent - robotic - real-world - dual-arm - video - vla - embodied intelligence size_categories: - n>1T --- ![1w](https://cdn-uploads.huggingface.co/production/uploads/68e91c238bba74b5f4b23508/NiNYy9M3FrxAX83jWtwlT.png) Boasting over 13,000 hours of cumulative data and 5 million+ clips, it ranks as the largest open-source embodied intelligence dataset in the industry. # Update Notes：Stage 3 data upload completed. 1. 13,000+ hours of pure dual-hand data with frame-level alignment latency < 1ms 2. Full high-precision trajectory reconstruction, breaking the limit of superficial open source, fully ready-to-use 3. 3,000+ contributors and 10,000+ real household scenarios with exceptional diversity 4. Comprehensive manipulation skill coverage to empower general embodied intelligence training We are committed to delivering high-quality data that drives industry progress, lowering innovation barriers and accelerating the implementation of robotic technologies.We will keep iterating and releasing next-generation EGO-view datasets with industry-leading quality and performance. # Update Notes：Stage 2 data upload completed. 1. 35,000 new clips featuring manual sorting & organizing of daily objects. 2. Enhanced data FOV for a fuller, more complete view of the lower environment. 3. More realistic & diverse targets & scenarios, covering flexible, irregular, various-sized objects in different storage boxes. 4. 40% higher trajectory accuracy and 50% better trajectory stability. We look forward to receiving more of your feedback. # Compared with other datasets, it has the following advantages: 1. Ample Data Volume & Strong Generalization Each skill is supported by sufficient data, collected from over 3,000 households and nearly 10,000 distinct fine-grained targets. It avoids simple repetitions and ensures robust generalization. ![20260104-225300](https://cdn-uploads.huggingface.co/production/uploads/68e91c238bba74b5f4b23508/NQSfkQ-ovsAHxL9bIm-BR.jpeg) 2. Authentic Scenarios & Focused Skills Captured from natural operations in real households, we avoid skill fragmentation that compromises quality. Instead, we focus on 10 key household scenarios and 30 core skills. 3. Bimanual & Long-duration Tasks Full recordings of the entire process of complex household chores and cleaning. Data collection by GenDAS Gripper. ![渲染.1681](https://cdn-uploads.huggingface.co/production/uploads/68e91c238bba74b5f4b23508/OKgECtEFSSRYBMJU526QR.png) 4. Multi-modal & High-quality Data Includes large-FOV raw images, trajectories, annotations and joint movements. Trajectory reconstruction ensures industry-leading precision and quality. # Dataset Statistics | Attribute | Value | |-------------------------|----------------------| | Median Clip Length | 210.0 seconds | | Storage Size | 95 TB | | Format | mcap | | Resolution | 1600*1296 | | Frame Rate | 30 fps | | Camera Type | Large FOV Fisheye Camera | | IMU | Yes 6-axis | | Tactile Array Spatial | Yes | | Array Spatial Resolution | 1 mm | | Device |Gen DAS Gripper | # Stage 1+2 Content： **We have uploaded the data of Stage 1&2 . This is only a small fraction and we will complete updates for the remaining skills as soon as possible.** 1. Stage 1 covers 12 skills across 4 major scenario tasks .Total duration: 950 hours, clips: 39,761, storage 3.45TB. 2. Stage 2 covers 4 major scenario tasks,it refers to the process of organizing a variety of cluttered items in a real household environment.Total duration: 653 hours, clips: 36,267, storage 1.92TB. | Task | Skill | |-------------------------------|--------------------------------| | Folding_Clothes_and_Zipper_Operations | fold_and_store_clothes | | | zip_clothes | | Cooking_and_Kitchen_Clean | clean_container | | | unscrew_bottle_cap_and_pour | | | clean_bowl | | Organize_Clutter | fold_and_store_shopping_bag | | | fold_towel | | | desktop_object_sorting | | | drawer_to_take_items | | Shoes_Handling | lace_up_shoes_with_both_hands | | | organize_scattered_shoes | | Clutter_Tidy-Up 【Stage2】 | irregular_object_clutter | | | flexible_grasping_and_sorting | | | carton_sorting_clutter | | | small_object_storage | And synchronize the progress across major social platforms.In addition to the data, we will also provide relevant support including format conversion and usage guidance，here is the link https://github.com/genrobot-ai/das-datakit. # Contact Us Any questions, suggestions or desired data collection scenarios/skills are welcome during usage. Let’s co-build this project to digitize all human skills. X：https://x.com/GenrobotAI Linkin：https://www.linkedin.com/company/108767412/admin/dashboard/ Email：opendata@genrobot.ai # Dataset Structure ``` The mcap files are stored in the final leaf folders of the file directory structure.Note: Each mcap file represents one piece of task data. ``` # Data Format Dual-arm tasks: robot0 and robot1 represent the left and right grippers respectively. Each gripper contains the following topics: ``` python /robot0/sensor/camera0/compressed # Fisheye camera image data — compressed and encoded in H.264 format. /robot0/sensor/camera0/camera_info # Fisheye Intrinsic and Extrinsic Parameters /robot0/sensor/imu # Inertial Measurement Unit (IMU) Data /robot0/sensor/magnetic_encoder # Magnetic encoder data: gripper opening distance /robot0/vio/eef_pose # Trajectory data ``` Topics are serialized using Protobuf for persistent storage /robot0/sensor/camera0/compressed: ``` protobuf // A compressed image message CompressedImage { // Timestamp of image google.protobuf.Timestamp timestamp = 1; // frame id string frame_id = 4; // Compressed image data, h264 video stream bytes data = 2; // Image format // Supported values: `webp`, `jpeg`, `png`, `h264` string format = 3; // common header, timestamp is inside it Header header = 8; } message Header { string module_name = 1; uint32 sequence_num = 2; uint64 timestamp = 3; string topic_name = 4; double expect_hz = 5; repeated Input inputs = 6; } ``` /robot0/sensor/camera0/camera_info: ``` protobuf // Camera calibration parameters message CameraCalibration { // not used google.protobuf.Timestamp timestamp = 1; // frame id string frame_id = 9; // Image width fixed32 width = 2; // Image height fixed32 height = 3; // Name of distortion model string distortion_model = 4; // Distortion parameters repeated double D = 5; // Intrinsic camera matrix (3x3 row-major matrix) // // A 3x3 row-major matrix for the raw (distorted) image. // // Projects 3D points in the camera coordinate frame to 2D pixel coordinates using the focal lengths (fx, fy) and principal point (cx, cy). // // ``` // [fx 0 cx] // K = [ 0 fy cy] // [ 0 0 1] // ``` repeated double K = 6; // length 9 // Rectification matrix (stereo cameras only, 3x3 row-major matrix) // // A rotation matrix aligning the camera coordinate system to the ideal stereo image plane so that epipolar lines in both stereo images are parallel. repeated double R = 7; // length 9 // Projection/camera matrix (stereo cameras only, 3x4 row-major matrix) // [fx' 0 cx' Tx] // P = [ 0 fy' cy' Ty] // [ 0 0 1 0] repeated double P = 8; // length 12 // transform from camera to base frame repeated double T_b_c = 10; // length 7, [tx ty tz qx qy qz qw] // common header Header header = 11; } ``` /robot0/sensor/imu: ``` protobuf // IMU message message IMUMeasurement { // common header arnold.common.proto.Header header = 1; // frame id string frame_id = 2; foxglove.Vector3 angular_velocity = 3; // Acceleration data in g-force units foxglove.Vector3 linear_acceleration = 4; // float temperature = 5; // repeated float angular_velocity_covariance = 6; // repeated float linear_acceleration_covariance = 7; } ``` /robot0/sensor/magnetic_encoder: ``` protobuf message MagneticEncoderMeasurement { // common header arnold.common.proto.Header header = 1; // frame id string frame_id = 2; // Distance between gripper fingers, 0-0.103m, 0 means closed double value = 3; } ``` /robot0/vio/eef_pose: ``` protobuf // A timestamped pose for an object or reference frame in 3D space message PoseInFrame { // not used google.protobuf.Timestamp timestamp = 1; // Frame id string frame_id = 2; // Pose in 3D space foxglove.Pose pose = 3; // linear vel foxglove.Vector3 linear_vel= 4; // angular_vel foxglove.Vector3 angular_vel = 5; // common header arnold.common.proto.Header header = 6; } ``` # How to Vis Data web view tool； ``` https://monitor.genrobot.click/#/index ``` # URDF ``` https://huggingface.co/datasets/genrobot2025/10Kh-RealOmin-OpenData/blob/main/DAS_Gripper_V3.zip ``` # Coordinate System ``` https://github.com/genrobot-ai/product-docs/blob/main/docs/md-productions-das-Matrix%20Studio%20Manual-en.md#5-data-intruction ``` # How to Load Data reference： ``` https://github.com/genrobot-ai/das-datakit ```