version stringclasses 1
value | episode_id stringclasses 1
value | record_time dict | device dict | time dict | files dict | streams dict | statistics dict | integrity dict |
|---|---|---|---|---|---|---|---|---|
1.0.0 | episode_20260712_171632 | {
"start_unix_us": 10616101522,
"end_unix_us": 10661815143,
"duration_sec": 45.713621405,
"common_start_us": 10616143880,
"common_end_us": 10661777199
} | {
"name": "dc",
"serial_number": "26004140",
"firmware_version": "v1.1.8-20260704-d83a3996"
} | {
"time_domain": "CLOCK_MONOTONIC_RAW",
"timestamp_unit": "us"
} | {
"hdf5": {
"path": "/data/states.hdf5",
"size_bytes": 1747928
},
"calibration": {
"rgb_left": "calibration/rgb_left.json",
"rgb_right": "calibration/rgb_right.json",
"fisheye": "calibration/fisheye.json",
"imu": "calibration/imu.json"
}
} | {
"rgb_left": {
"enabled": true,
"type": "camera",
"encoding": "H264",
"width": 1600,
"height": 1200,
"fps": 60,
"frame_count": 2741,
"segments": [
{
"file_id": 1,
"path": "episode_20260712_171632/videos/rgb_left/0001.mp4",
"size_bytes": 23135987,
... | {
"rgb_left_frames_lost_count": 2,
"rgb_right_frames_lost_count": 2,
"fisheye_frames_lost_count": 1,
"fisheye_B_frames_lost_count": 0,
"imu_lost_count": 0,
"imu_left_lost_count": 0,
"imu_right_lost_count": 0,
"slam_lost_count": 0,
"hand_left_slam_lost_count": 0,
"hand_right_slam_lost_count": 0
} | {
"episode_complete": true,
"unexpected_shutdown": false
} |
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
1000-Segments-6-camera-Egocentric-Embodied-AI-Dataset
Description
10,000-Hour Egocentric Full-Body Multimodal Dataset Purpose-built for fine-grained embodied AI manipulation training, spanning diverse real-world scenarios with high-definition stereoscopic video, full-body joint poses, and high-density semantic annotations.
For more details, please refer to the link: https://www.nexdata.ai/datasets/embodied-ai/2236?source=Huggingface
Specifications
Data size
10,000-Hour Egocentric Full-Body Multimodal Dataset
Data Distribution
Covers residential, retail & office scenarios (kitchen, bedroom, living room, supermarket, office) with diverse real-life tasks: meal prep, cleaning, storage, garment care, merchandising & picking
Data Content
Each sample includes spatiotemporally aligned 4K stereo video, camera calibration params, 76-point full-body pose & step-by-step annotations
Capture Solution
Adopts PICO 4 Ultra head-mounted stereo camera + wrist & ankle IMU motion capture solution
Data Annotation
Supports dense semantic & action-level annotations; all data passes multi-stage quality control reviews
Data Quality
Supports 4096×1536 / 30fps HD video output, tracks 24 torso joints and 52 hand joints, with frame-wise dense annotations and full-process quality control
Full Dataset Access
The complete dataset is available upon request. Reach out to us to learn more and submit an access request.
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