Sourcing spec
Sourcing egocentric kitchen video
An egocentric kitchen video dataset is useful for household robotics, VLA, and world-model teams. When sourcing it, specify first-person cooking, cleaning, object handling, and cabinet interaction video, capture in residential kitchens with varied layouts and appliances, and task label, appliance type, object set, consent artifact, and clip boundary so supplier samples can be reviewed before adoption.
Quick facts
- Closest public corpus
- EPIC-KITCHENS-100 — 100 hours Full HD across 45 kitchens in 4 cities, 90,000 action segments (2022 release; HD-EPIC validation March 2025)
- License
- EPIC-KITCHENS is CC BY-NC 4.0 — non-commercial only, blocks commercial training.
- Adjacent corpus
- Ego4D — 3,670 hours general daily-life egocentric (subset of which is kitchen activity); requires Data Use Agreement.
- Commercial gap
- Buyers needing the right kitchen layout, appliance set, recipe, or commercial license cannot start from public corpora alone.
- Spec checklist
- Appliance manifest, recipe/task labels, hand visibility, contributor consent, commercial license, accepted/rejected episode samples.
Comparison
| Option | Strength | Gap |
|---|---|---|
| Generic dataset | Fast discovery | Usually lacks the buyer's rights and metadata |
| Public benchmark | Academic baseline | Often not fit for commercial deployment |
| truelabel sourcing | Spec-matched supplier samples | Needs buyer review before scale-up |
Dataset requirements
Buyers should specify first-person cooking, cleaning, object handling, and cabinet interaction video, accepted scenes in residential kitchens with varied layouts and appliances, consent rules, and a pilot package large enough to include accepted clips, rejected clips, metadata, consent/provenance artifacts, and edge cases for buyer review. The exact metadata package should include: task label, appliance type, object set, consent artifact, and clip boundary [1]. The Datasheets framework spells out which dataset-documentation questions matter before any commercial training program begins.
[2]"The machine learning community currently has no standardized process for documenting datasets, which can lead to severe consequences in high-stakes domains."
Best-fit buyers
The strongest fit is household robotics, VLA, and world-model teams [3]. It can also work as a smaller eval set before a larger net-new capture program when the buyer defines the held-out tasks, failure cases, and acceptance rubric in writing.
Egocentric kitchen video sample package
A credible egocentric kitchen video supplier should provide a sample package that includes raw files, a manifest, capture context, and these critical metadata fields: task label, appliance type, object set, consent artifact, and clip boundary [4]. The buyer should be able to inspect accepted and rejected examples to confirm whether first-person cooking, cleaning, object handling, and cabinet interaction video actually appears in residential kitchens with varied layouts and appliances, not just trust a verbal description of the inventory.
Egocentric kitchen video licensing check
The licensing review for egocentric kitchen video dataset should confirm whether the data is off-the-shelf or net-new, whether it can be used for commercial model training, whether contributors or sites consented, and whether the supplier can reproduce the same rights package for the full delivery [5]. A well-scoped buyer reviews multiple supplier samples before approving scale; without those checks, an apparently useful dataset can become a legal or procurement blocker.
Environment-specific sourcing checklist
This sourcing page should translate an environment into concrete coverage, consent/provenance, and QA requirements. Specify task phases, camera mount, objects/equipment, locations, bystander and site-permission handling, redaction rules, accepted/rejected clips, manifest fields, and pilot review gates.
| Area | Specify | Reject if |
|---|---|---|
| Coverage | tasks, locations, object/equipment classes | generic footage misses target workflow |
| Capture | wearable/head/chest/glasses rig, FPS, audio policy | hands/action out of frame |
| Governance | consent, site permission, redaction, retention | no artifact trail |
| Delivery | MP4+JSON, labels, manifest, QA notes | sample cannot be audited |
Public references vs custom sourcing
Public egocentric datasets can provide benchmark context, but they rarely cover a buyer's exact site, object mix, workflow, consent chain, commercial rights, or delivery format. Use them for scoping, then request custom capture through sourcing intake when deployment fit matters, and send rights questions to egocentric data licensing.
Accepted and rejected sample package
A useful sourcing pilot includes accepted clips, rejected clips, task labels, camera mount notes, environment metadata, privacy/redaction QA results, consent or site artifacts for review, and a delivery manifest. Kitchen pilots should include accepted clips with appliance/object state changes, verb-noun task labels, food-safety or cleanliness notes where relevant, and rejected clips for faces, minors, private documents, ambiguous object state, or missing location consent. Those examples let the buyer tune the spec before volume and compare adjacent warehouse, kitchen, and industrial constraints before scale-up.
Related pages
Use these to move from category-level context into specific task, dataset, format, and comparison detail.
External references and source context
- Datasheets for Datasets
Supports the dataset-requirements framework dimensions: dataset motivation, composition, collection process, recommended uses, and license review.
arXiv ↩ - Datasheets for Datasets
Datasheets for Datasets defines the consent, provenance, and intended-use questions buyers must ask before commercial training; quoted verbatim in the dataset-requirements section.
arXiv ↩ - Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Open X-Embodiment establishes the cross-embodiment robotics pretraining baseline — a useful reference for buyer fit when mapping a deployment-specific dataset onto a generalist policy.
arXiv ↩ - Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI
Data Cards capture dataset origins, development, intent, and ethical considerations buyers can attach to each delivered batch for procurement audit.
arXiv ↩ - encord
Commercial vendors deliver licensed dataset collection programs with explicit contributor consent, rights, and per-batch documentation buyers can audit before scale.
encord.com ↩ - truelabel physical AI data marketplace bounty intake
Internal contextual link to Truelabel's physical AI and robotics data marketplace.
truelabel.ai - truelabel egocentric data glossary
Internal contextual link to the egocentric data definition.
truelabel.ai - truelabel VLA training data sourcing
Internal contextual link to VLA training data sourcing.
truelabel.ai - truelabel warehouse robotics data sourcing
Internal contextual link to warehouse robotics data sourcing.
truelabel.ai - truelabel kitchen manipulation data sourcing
Internal contextual link to kitchen manipulation data sourcing.
truelabel.ai - truelabel LeRobot format guide
Internal contextual link to the LeRobot format guide.
truelabel.ai - truelabel LeRobot dataset alternative comparison
Internal contextual link to the LeRobot dataset alternative comparison.
truelabel.ai - truelabel eval data for robotics hub
Internal contextual link to robotics eval data sourcing.
truelabel.ai - truelabel teleoperation training-data page
Internal contextual link to teleoperation training data sourcing.
truelabel.ai - truelabel robot demonstrations training-data page
Internal contextual link to robot demonstration training data sourcing.
truelabel.ai - truelabel hand-object interaction data page
Internal contextual link to hand-object interaction training data requirements.
truelabel.ai - truelabel egocentric video datasets hub
Internal contextual link to the egocentric video datasets hub.
truelabel.ai
FAQ
What is an egocentric kitchen video dataset?
It is a dataset focused on residential kitchens with varied layouts and appliances using first-person cooking, cleaning, object handling, and cabinet interaction video. The buyer should require task label, appliance type, object set, consent artifact, and clip boundary for provenance and training-readiness.
Can this be off-the-shelf?
Yes. Suppliers can respond with existing datasets if they can prove rights, consent, and metadata coverage for the buyer's spec.
What makes the dataset usable for training?
The dataset needs consistent files, task labels, timestamps or clip boundaries, rights, consent artifacts, and a delivery manifest that matches the buyer's pipeline.
How does truelabel route this request?
truelabel routes the request to suppliers whose capability profile matches the requested modality, environment, geography, rights, and delivery format.
Looking for egocentric kitchen video dataset?
Specify modality, task, environment, requested rights posture, and delivery format. Truelabel routes the request to candidate capture partners and helps scope consent/provenance artifacts and commercial licensing requirements for buyer review before delivery.
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