Sourcing teleop kitchen data

Quick facts

RoboSet (kitchen)

28,500 trajectories — 9,500 teleoperated and 19,000 kinesthetic playback — across kitchen activities (MIT license).

ALOHA

$20k bimanual teleoperation rig running ACT — 50 demonstrations per task, 12–20 second episodes at 50Hz; widely used for kitchen tasks (RSS 2023).

DROID (subset)

Franka Panda manipulation across 564 scenes — kitchens are part of the scene set; total corpus 76,000 trajectories / 350h (2024).

Commercial gap

Public teleop kitchen corpora are research-grade; appliance set, robot embodiment, and contributor consent rarely match a commercial buyer's deployment.

Spec checklist

Robot embodiment, gripper type, appliance manifest, task instructions, success/failure flags, episode boundary metadata, sync between cameras and state.

Comparison

Dataset requirements

Buyers should specify robot teleoperation traces with wrist or external video, accepted scenes in kitchen counters, cabinets, drawers, appliances, and utensils, a minimum of 10 hours of useful captured volume, consent rules, and the exact metadata package: task instruction, object set, robot state, success flag, and episode boundary ^[1]. The Datasheets framework spells out which dataset-documentation questions matter before any commercial training program begins.

"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 home robotics teams validating household manipulation behavior ^[3]. It can also work as a smaller eval set — typically 100 to 500 episodes — before a larger net-new capture program.

Teleop kitchen data sample package

A credible teleop kitchen data supplier should provide a sample package that includes raw files, a manifest, capture context, and these critical metadata fields: task instruction, object set, robot state, success flag, and episode boundary ^[4]. Across at least 10 representative episodes, the buyer should be able to inspect whether robot teleoperation traces with wrist or external video actually appears in kitchen counters, cabinets, drawers, appliances, and utensils, not just trust a verbal description of the inventory.

Teleop kitchen data licensing check

The licensing review for teleoperation kitchen 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 typically reviews 3 to 5 supplier deliveries before approving scale; without those checks, an apparently useful dataset can become a legal or procurement blocker.

Related pages

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

External references and source context

1. Datasheets for Datasets

   Supports the dataset-requirements framework dimensions: dataset motivation, composition, collection process, recommended uses, and license review.

   arXiv ↩
2. 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 ↩
3. 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 ↩
4. 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 ↩
5. encord

   Commercial vendors deliver licensed dataset collection programs with explicit contributor consent, rights, and per-batch documentation buyers can audit before scale.

   encord.com ↩

FAQ