Boasting over 10,000 hours of cumulative data and 1 million+ clips, it ranks as the largest open-source embodied intelligence dataset in the industry.

Compared with other datasets, it has the following advantages:

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.
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.
Bimanual & Long-duration Tasks Full recordings of the entire process of complex household chores and cleaning. Data collection by GenDAS Gripper.
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 Content：

We have uploaded the data of Stage 1. This is only a small fraction and we will complete updates for the remaining skills as soon as possible. Stage 1 covers 12 skills across 4 major scenario tasks .Total duration: 950 hours, clips: 39,761, storage 3.45TB.

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	desktop_object_sorting
	fold_towel
	fold_and_store_shopping_bag
	drawer_to_take_items
	drawer_to_place_items
Shoes_Handling	lace_up_shoes_with_both_hands
	organize_scattered_shoes

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：[email protected]

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:

/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:

// 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:

// 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:

// 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:

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:

// 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

How to Load Data

reference：

https://github.com/genrobot-ai/das-datakit

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