--- license: mit language: - en - es size_categories: - n<1K task_categories: - video-classification - robotics tags: - egocentric - pov - first-person - robotics - human-demonstration - vla - wam - world-action-model - manipulation - imitation-learning - behavioral-cloning - industrial - workplace - factory - manufacturing - retail - construction - field-services - vocational - pi0 - pi1 - openvla - rt-2 - groot - cosmos pretty_name: Industrial & Workplace Egocentric Video — 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](https://huggingface.co/datasets/TrainThemAI/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](https://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 ```bibtex @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](https://trainthemai.com) - **LinkedIn:** [linkedin.com/company/trainthemai](https://www.linkedin.com/company/110815412)