Task data

Kitchen tasks training data

Kitchen tasks training data helps physical AI teams collect scoped examples in residential kitchens, counters, cabinets, sinks, and appliances. When sourcing it, specify egocentric video, object states, hand pose, and environment metadata, target volume, delivery format, rights, consent, and QA rules for task start/end boundaries, object identity, lighting, and consent for private spaces.

Updated 2026-05-04

By truelabel

Reviewed by truelabel · May 4, 2026

kitchen robot dataset

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Quick facts

Task: Kitchen tasks
Modality: egocentric video, object states, hand pose, and environment metadata
Environment: residential kitchens, counters, cabinets, sinks, and appliances
Volume: 25-75 hours of repeated kitchen task sequences
Format: MP4 plus JSONL or HDF5 task manifest
QA: task start/end boundaries, object identity, lighting, and consent for private spaces

Comparison

Source	Use	Limitation
Public dataset	Research baseline	academic kitchen datasets often have non-commercial licenses or limited task diversity
Internal capture	Maximum control	Slow setup and high fixed cost
truelabel sourcing	Spec-matched supplier response	Requires clear acceptance criteria

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."
— from Dataset documentation — clvrai.github.io

^[5]

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 from the sample to 25-75 hours of repeated kitchen task sequences ^[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.

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

Training data tasksTask hub Data provenance for physical AIRelated page What is physical AI training data?Related page Sourcing egocentric kitchen videoRelated page Sourcing teleop kitchen dataRelated page Assembly training dataTask-specific requirements Bimanual manipulation training dataTask-specific requirements Dexterous manipulation training dataTask-specific requirements

External references and source context

Project site
EPIC-KITCHENS is an egocentric dataset for kitchen activities and object interactions.
epic-kitchens.github.io ↩
Dataset page
LIBERO datasets include manipulation demonstrations useful for household task data framing.
libero-project.github.io ↩
Hugging Face organization
AgiBot World is a large robotics dataset source relevant to everyday manipulation tasks.
Hugging Face ↩
Project site
RoboCasa provides household and kitchen-like manipulation task environments.
robocasa.ai ↩
Dataset documentation
FurnitureBench dataset documentation covers manipulation demonstrations with structured robot observations and actions.
clvrai.github.io ↩
TensorFlow Datasets catalog
TACO Play is a dataset catalog entry for robot play data useful in kitchen task contexts.
tensorflow.org ↩

FAQ

What is kitchen robot dataset?

kitchen robot dataset refers to data collected for residential kitchens, counters, cabinets, sinks, and appliances. It usually includes egocentric video, object states, hand pose, and environment metadata, metadata, and task outcomes that help train or evaluate physical AI systems.

What should a sourcing request include?

It should include task definition, environment, modality, volume, format, rights, consent, budget, deadline, and QA checks such as task start/end boundaries, object identity, lighting, and consent for private spaces.

What format should buyers request?

MP4 plus JSONL or HDF5 task manifest is the recommended starting point, but truelabel can route buyer-defined schemas when the training pipeline needs a custom layout.

Can this be exclusive?

Yes. Net-new sourcing requests can request exclusive commercial rights, while off-the-shelf datasets are usually non-exclusive unless the buyer explicitly purchases exclusivity.

Sourcing data for kitchen robot dataset

Specify the environment, scale, and rights you need. Truelabel matches you with capture partners delivering kitchen robot dataset data with consent artifacts and commercial licensing attached.

Request kitchen tasks training data