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Industrial & Workplace Egocentric Video — FHD Samples
21 clips of first-person video of real industrial and workplace tasks, captured with a head-mounted smartphone. Released by TrainThemAI for training Vision-Language-Action (VLA) models, World Action Models (WAM), and humanoid manipulation policies — π0, π1, OpenVLA, RT-2, GR00T, Cosmos, DreamZero.
Fully rights-cleared, MIT-licensed, and representative of our production capture pipeline.
Companion to POV Egocentric Video — Robotics FHD Samples (residential / household tasks).
📞 Production-scale data — talk to us
We collect egocentric video at scale for embodied-AI teams.
- 500+ active operators across Latin America and the Philippines (live as of May 2026)
- Custom activity coverage — household, workplace (manufacturing, retail, hospitality, construction), or specialty domains
- Per-project QC against client-specified rejection criteria
- Typical engagement: 100–5,000 hours, 3–12 week delivery windows
- Ego + wearable hardware dataset coming June 2026 — first-person video paired with hand pose and wrist trajectory tracking, for action-labeled data at ~1/10 the cost of robot teleoperation
📧 hello@trainthemai.com — we respond within one business day.
🌐 trainthemai.com
What's in the sample
21 clips, ~5.1 GB total, spanning real industrial and workplace environments:
| Domain | Clips |
|---|---|
| Manufacturing & factory work | Lathe / handwheel spindle, Copper factory, Metal fabricator factory, Packaging factory, Textile factory, Ceramics, Woodworking, Factory (generic) |
| Retail, storefronts & food service | Retail, Storefronts, Pharmacy, Restaurants & cafes, Barista |
| Personal services | Hair / nail / lash services (×2), Tailor, Laundromat |
| Construction & heavy equipment | Construction, Heavy equipment |
| Field & outdoor | Farms, Transit |
Technical specifications
| Spec | Value |
|---|---|
| Resolution | 1080p (1920×1080) |
| Frame rate | 30 fps |
| Codecs | H.264 / HEVC video, AAC audio |
| Camera | Smartphone with ultrawide (0.5×) lens |
| Mount | Head strap at forehead or eye level, angled ~45° downward |
| Face | Never on-camera by design |
| Hands in frame | >90% of recording duration |
| Action density | Continuous manipulation in operational settings, idle pauses kept under 10 seconds |
| Clip length | 30 seconds – 6 minutes (varies by task) |
| Environments | Real factories, workshops, retail floors, service businesses — operator-owned or operator-affiliated worksites, natural lighting |
| Total | 21 clips, ~5.1 GB, MIT license |
Per-clip metadata (JSON sidecars)
Every clip ships with a companion JSON sidecar carrying camera and capture metadata, named to match the video — e.g. Transit.MP4 → Transit.json, fetched from the same path. A combined metadata_manifest.json at the repo root indexes every clip.
Each sidecar contains:
| Field | Notes |
|---|---|
session_uuid |
Stable per-clip identifier |
environment_type |
commercial |
country |
ISO code where embedded; unspecified otherwise (these clips carry no GPS) |
camera_model |
Detected device class — GoPro HERO12 Black where onboard telemetry is present, smartphone (ultrawide 0.5×) otherwise |
focal_length |
Physical focal length in mm |
distortion_coefficients |
OpenCV radial/tangential [k1, k2, p1, p2, k3] |
capture |
resolution, frame rate, codec, lens |
imu_available / pose_available |
true for GoPro clips (onboard accelerometer + gyroscope and orientation/gravity telemetry); false for smartphone clips |
calibration_status |
reference_nominal — intrinsics are reference values for the detected device class. Per-unit checkerboard calibration is available on request for production engagements. |
These fields follow common egocentric-data intake requirements, so the samples can be evaluated directly against a production spec.
Why egocentric for embodied AI
The first-person, head-mounted perspective closely matches a humanoid robot's head-camera viewpoint, which makes this format especially well-suited for:
- Industrial cobot / mobile manipulator policies — assembly, packaging, sorting, machine tending, material handling
- Service-robotics fine-tuning — retail floor tasks, food service, personal-services manipulation
- VLA / WAM pretraining on workplace task distributions, not just home or lab
- Behavioral cloning for vocational skills — lathe operation, woodworking, textile handling, tailoring
- Benchmarking workplace-egocentric perception, tool-use recognition, and procedural action segmentation
How this compares to public alternatives for industrial / workplace egocentric video:
| Dataset | Scale | Focus | License | Production-extensible? |
|---|---|---|---|---|
| This sample | 21 clips / ~5.1 GB | Real factories, workshops, services | MIT | ✅ commercial pipeline |
| HoloAssist (Microsoft) | ~166 hr | Industrial maintenance / assistance | Research | ❌ |
| Assembly101 | ~513 hr | Toy assembly only | Academic | ❌ |
| Ego4D | ~3,600 hr | Broad ego, low industrial density | Academic license | ❌ |
| EgoProceL | ~62 hr | Procedural tasks, academic | Academic | ❌ |
For research benchmarking, the above are useful. For commercial-grade training data in real industrial and workplace settings at the scale and spec you need, that's where TrainThemAI comes in.
License
MIT — free for any use including commercial, research, redistribution, and model training. Attribution to TrainThemAI appreciated but not required.
Citation
@misc{trainthemai_industrial_workplace_egocentric_2026,
author = {TrainThemAI},
title = {Industrial \& Workplace Egocentric Video --- FHD Samples},
year = {2026},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/TrainThemAI/Industrial-Workplace-Egocentric-FHD-Samples}
}
Contact
- Sales / contract data: hello@trainthemai.com
- Web: trainthemai.com
- LinkedIn: linkedin.com/company/trainthemai
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