Kitchen tasks training data

Quick facts

Task

Kitchen tasks

Modality

egocentric video, object states, hand pose, and environment metadata

Environment

residential kitchens, counters, cabinets, sinks, and appliances

Volume

buyer-approved coverage across privacy, state-change labels, and task variation

Format

MP4 plus JSONL or HDF5 task manifest

QA

task start/end boundaries, object identity, lighting, and consent for private spaces

Comparison

What to specify for kitchen tasks

The sourcing request should define task boundaries, capture setting, actor or robot requirements, accepted modalities, MP4 plus JSONL or HDF5 task manifest delivery expectations, rights, consent, and what counts as an accepted sample. Registry sources show that task data is only reusable when collection setup and task distribution are explicit ^[1]. Buyers should also pin delivery expectations to formats and documentation they can validate before scale ^[2].

Why public data is usually not enough

academic kitchen datasets often have non-commercial licenses or limited task diversity. Benchmark and vendor sources show that task labels, rights, and capture context are not interchangeable across deployments ^[3]. A buyer-specific request lets the team request the exact object set, environment, geography, and QA rubric needed for model training or evaluation.

Kitchen tasks buyer scenario

A realistic kitchen tasks request starts when a robotics team has a model behavior that fails in residential kitchens, counters, cabinets, sinks, and appliances. The team does not just need more video; it needs examples where task start/end boundaries, object identity, lighting, and consent for private spaces can be verified repeatedly ^[4].

"FurnitureBench dataset documentation covers manipulation demonstrations with structured robot observations and actions."

That means the supplier must show the requested egocentric video, object states, hand pose, and environment metadata, prove the capture context, and deliver MP4 plus JSONL or HDF5 task manifest in a way the buyer can test before scaling.

Kitchen tasks sample acceptance criteria

A useful sample for kitchen robot dataset should include at least one accepted episode, one borderline or failed example, a complete metadata manifest, and a note explaining how the supplier would scale only to buyer-approved coverage across privacy, state-change labels, and task variation ^[6]. If the sample cannot show task start/end boundaries, object identity, lighting, and consent for private spaces, the buyer should reject it before funding a larger batch.

Kitchen tasks task taxonomy and coverage

A kitchen robot dataset request should be task-specific, not template-level. Define the sub-tasks, capture viewpoints, object/environment coverage, labels, failure modes, and pilot acceptance rules before asking a supplier to scale. A good sample includes verb-noun actions, object state before/after, private-space governance artifacts, and clips rejected for privacy or label ambiguity.

Kitchen tasks accepted and rejected examples

Kitchen-task data becomes unusable when it ignores privacy and state change. Require consent/location artifacts, appliance/object state labels, and rejected clips for faces, screens, minors, or private documents where policy demands exclusion. Use licensing/provenance review for human or workplace footage, and route robotics-ready requests through the robot training data marketplace once the pilot schema is clear.

Kitchen tasks pilot manifest fields

For Kitchen tasks, the pilot manifest should include task phase, environment, object or route class, camera/sensor keys, action/state fields when applicable, timestamps, outcome label, failure reason, reviewer decision, rights/provenance files, and the target delivery format. A good sample includes verb-noun actions, object state before/after, private-space governance artifacts, and clips rejected for privacy or label ambiguity. The manifest is the bridge between supplier footage and buyer QA: if a reviewer cannot reproduce why a sample passed or failed, the dataset is not ready for scale-up.

Public datasets as references, not drop-in commercial supply

Public robotics datasets can guide schema and benchmark expectations, but commercial use, embodiment fit, action-state coverage, and consent/provenance are dataset-specific. Treat them as references unless official terms and buyer review support the intended use.

Pilot package before scale-up

Require a small loadable pilot with raw media/logs, manifest, labels, accepted and rejected samples, consent/provenance artifacts, and validation in the target format. Reject missing fields, broken sync, unclear boundaries, unsupported rights, and samples with only clean successes. For warehouse, kitchen, or industrial variants, compare the nearest warehouse, kitchen, or industrial sourcing spec before scaling.

Related pages

Use these to move from category-level context into specific task, dataset, format, and comparison detail.

External references and source context

1. EPIC-KITCHENS project site

   EPIC-KITCHENS is an egocentric dataset for kitchen activities and object interactions.

   epic-kitchens.github.io ↩
2. Dataset page

   LIBERO datasets include manipulation demonstrations useful for household task data framing.

   libero-project.github.io ↩
3. Hugging Face organization

   AgiBot World is a large robotics dataset source relevant to everyday manipulation tasks.

   Hugging Face ↩
4. Project site

   RoboCasa provides household and kitchen-like manipulation task environments.

   robocasa.ai ↩
5. Dataset documentation

   FurnitureBench dataset documentation covers manipulation demonstrations with structured robot observations and actions.

   clvrai.github.io ↩
6. TensorFlow Datasets catalog

   TACO Play is a dataset catalog entry for robot play data useful in kitchen task contexts.

   tensorflow.org ↩
7. truelabel egocentric data glossary

   Internal contextual link to the egocentric data definition.

   truelabel.ai
8. truelabel sourcing brief intake

   Internal contextual link to Truelabel's sourcing intake workflow.

   truelabel.ai
9. truelabel VLA training data sourcing

   Internal contextual link to VLA training data sourcing.

   truelabel.ai
10. truelabel warehouse robotics data sourcing

    Internal contextual link to warehouse robotics data sourcing.

    truelabel.ai
11. truelabel kitchen manipulation data sourcing

    Internal contextual link to kitchen manipulation data sourcing.

    truelabel.ai
12. truelabel LeRobot format guide

    Internal contextual link to the LeRobot format guide.

    truelabel.ai
13. truelabel LeRobot dataset alternative comparison

    Internal contextual link to the LeRobot dataset alternative comparison.

    truelabel.ai
14. truelabel eval data for robotics hub

    Internal contextual link to robotics eval data sourcing.

    truelabel.ai
15. truelabel teleoperation training-data page

    Internal contextual link to teleoperation training data sourcing.

    truelabel.ai
16. truelabel robot demonstrations training-data page

    Internal contextual link to robot demonstration training data sourcing.

    truelabel.ai
17. truelabel hand-object interaction data page

    Internal contextual link to hand-object interaction training data requirements.

    truelabel.ai
18. truelabel egocentric video datasets hub

    Internal contextual link to the egocentric video datasets hub.

    truelabel.ai

FAQ