Human Archive
Human Archive is modeling human sensorimotor intelligence at scale. We currently collect 2,000+ hours of this multimodal dataset per week, making this the largest and first dataset of its kind.
We’re backed by Y Combinator and engineers from OpenAI, BAIR, SAIL, Anduril Industries, Mercor, NVIDIA, Jane Street, Google, DoorDash AI Research, Reevo, AfterQuery, and the investors behind AMI Labs.
Follow us on X
To purchase the full dataset, find time here
500hr-samples
An egocentric human activity dataset in LeRobot v3 spanning diverse real-world environments, including hotels, restaurants, warehouses, homes, factories, and other commercial and residential settings.
Dataset Overview
| Metric | Value |
|---|---|
| Total persons | 5,562 |
| Total video segments | 10,633 |
| Total duration | 499.9 hours |
| Total size | ~2.5 TB |
| Resolution | 1920×1080 |
| Codec | H.264 |
| Frame rate | 30 FPS |
| Audio | None |
| Clip duration range | 30s – 598s |
| Unique environments | 181 |
| Unique tasks | 753 |
Directory Structure
s3://500hr-samples/
├── commercial/
│ ├── factory/ # 1,131 persons · 2,013 segments · 106.1 hours
│ │ ├── person{N}/
│ │ │ ├── person{N}_segments.json
│ │ │ ├── person{N}_segment1.mp4
│ │ │ ├── person{N}_segment2.mp4
│ │ │ └── ...
│ │ └── ...
│ └── hospitality/ # 3,619 persons · 7,397 segments · 340.6 hours
│ ├── person{N}/
│ │ ├── person{N}_segments.json
│ │ ├── person{N}_segment1.mp4
│ │ └── ...
│ └── ...
├── residential/ # 812 persons · 1,223 segments · 53.2 hours
│ ├── person{N}/
│ │ ├── person{N}_segments.json
│ │ ├── person{N}_segment1.mp4
│ │ └── ...
│ └── ...
└── README.md
Split Summary
| Split | Persons | Segments | Hours | % of Total |
|---|---|---|---|---|
| commercial/factory | 1,131 | 2,013 | 106.1 | 21.2% |
| commercial/hospitality | 3,619 | 7,397 | 340.6 | 68.1% |
| residential | 812 | 1,223 | 53.2 | 10.6% |
| Total | 5,562 | 10,633 | 499.9 | 100% |
Per-Person JSON Schema
Each person folder contains a person{N}_segments.json file with the following schema:
{
"person_id": "person123",
"total_segments": 3,
"total_duration_sec": 456.0,
"segments": [
{
"person_id": "person123",
"video_index": "segment1",
"duration_sec": 152.0,
"task": "cutting_vegetables",
"environment": "commercial_kitchen",
"width": 1920,
"height": 1080,
"fps": 30.0,
"size_bytes": 245678901,
"codec": "h264"
}
]
}
Field Descriptions
| Field | Type | Description |
|---|---|---|
person_id |
string | Unique person identifier (matches folder name) |
total_segments |
int | Number of video segments for this person |
total_duration_sec |
float | Sum of all segment durations in seconds |
segments[].video_index |
string | Segment identifier (segment1, segment2, ...) |
segments[].duration_sec |
float | Duration in seconds (verified by ffprobe) |
segments[].task |
string | Activity label (e.g., washing_dishes, assembling_parts) |
segments[].environment |
string | Environment label (e.g., commercial_kitchen, factory_floor) |
segments[].width |
int | Video width in pixels |
segments[].height |
int | Video height in pixels |
segments[].fps |
float | Frames per second |
segments[].size_bytes |
int | File size in bytes |
segments[].codec |
string | Video codec |
Segments Per Person Distribution
| Segments | Persons | % |
|---|---|---|
| 1 | 3,028 | 54.4% |
| 2 | 1,142 | 20.5% |
| 3 | 695 | 12.5% |
| 4 | 406 | 7.3% |
| 5 | 174 | 3.1% |
| 6 | 86 | 1.5% |
| 7 | 22 | 0.4% |
| 8 | 9 | 0.2% |
Top 30 Environments
| Environment | Clips | Hours |
|---|---|---|
| commercial_kitchen | 3,679 | 174.7 |
| factory_floor | 1,119 | 58.6 |
| workshop | 1,089 | 57.7 |
| kitchen | 1,036 | 42.3 |
| warehouse | 482 | 23.4 |
| restaurant | 464 | 23.3 |
| hotel_room | 262 | 9.2 |
| bathroom | 249 | 8.9 |
| office | 176 | 9.0 |
| outdoor_area | 171 | 8.1 |
| home | 93 | 2.6 |
| home_kitchen | 85 | 3.6 |
| cafeteria | 83 | 2.9 |
| bar | 75 | 2.3 |
| banquet_hall | 72 | 3.5 |
| laundry_room | 71 | 4.0 |
| hallway | 62 | 3.2 |
| bedroom | 60 | 2.7 |
| lobby | 60 | 3.2 |
| hotel_lobby | 55 | 2.1 |
| balcony | 53 | 2.1 |
| auto_shop | 48 | 1.8 |
| living_room | 44 | 2.2 |
| restaurant_patio | 35 | 1.5 |
| cafe | 35 | 1.1 |
| conference_room | 31 | 1.3 |
| residential_room | 31 | 1.6 |
| auto_repair_shop | 29 | 0.9 |
| dining_hall | 26 | 1.2 |
Top 30 Tasks
| Task | Clips | Hours |
|---|---|---|
| washing_dishes | 793 | 32.6 |
| wiping_surface | 770 | 31.9 |
| cutting_vegetables | 632 | 33.3 |
| cooking_food | 373 | 16.4 |
| packaging_items | 267 | 11.8 |
| assembling_parts | 264 | 14.1 |
| assembling_footwear | 256 | 13.0 |
| frying_food | 236 | 9.4 |
| portioning_food | 234 | 9.9 |
| operating_machine | 180 | 10.2 |
| mopping_floor | 170 | 8.9 |
| sorting_produce | 150 | 8.0 |
| sweeping_floor | 145 | 6.5 |
| moving_objects | 136 | 4.8 |
| cleaning_floor | 134 | 6.2 |
| folding_clothes | 134 | 4.1 |
| kneading_dough | 119 | 4.4 |
| plating_food | 112 | 5.3 |
| serving_food | 106 | 3.9 |
| washing_mop | 103 | 3.8 |
| arranging_containers | 100 | 4.0 |
| eating_food | 95 | 2.6 |
| scrubbing_surface | 94 | 4.2 |
| preparing_food | 92 | 5.5 |
| clearing_table | 87 | 3.5 |
| packaging_food | 86 | 4.5 |
| polishing_glassware | 72 | 2.4 |
| servicing_machinery | 70 | 2.9 |
| making_bed | 65 | 2.7 |
Data Quality
All 10,633 video files have been individually verified:
- Every MP4 passes ffprobe validation (zero corrupt files)
- No audio streams in any video
- All duration, resolution, FPS, codec, and file size metadata in JSONs match actual values
- All segment indices are sequential (segment1, segment2, ...)
- All
total_duration_secfields match the sum of segment durations - No clips shorter than 30 seconds
- Every person has both a valid JSON and corresponding MP4 file(s)
- No duplicate videos across the entire bucket
Source
Videos are derived from egocentric recordings captured with chest-mounted cameras at 1920×1080, 30 FPS. Original footage was processed through a QA pipeline using Gemini AI for activity segmentation and quality scoring, then cut into individual segments.