TrainThemAI/Industrial-Workplace-Egocentric-FHD-Samples

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.
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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