Kled: Tools in Use
Collection
Video of people doing ordinary tasks with their hands: cooking, folding, repairing, sorting, cleaning. For robot learning and world models. • 24 items • Updated
Datasets:
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Dataset Card for Drawer Reorganization Video
People emptying a drawer, sorting the contents, cleaning it and putting everything back in order, faces out of frame.
Collected through the Kled app by consenting contributors and licensed for commercial training. The files are delivered by Kled under license, not downloaded from this repository. Use Request a sample set at the top of this page, or write to team@kled.ai. Catalog entry: kled.ai/datasets/organize-a-drawer.
Dataset Details
Dataset Description
Everything out, items sorted into groups, the drawer wiped, everything returned in order, filmed with faces out of frame in homes on three continents. The contents are the real miscellany of household drawers: small, mixed, often novel objects in dense arrangements, with before, during and after states of the same space. No object, grouping or action labels are included. Tidying is a task people want from a home robot and one with little usable video, because it requires deciding what things are and where they belong as well as moving them; the before-and-after structure suits placement evaluation and long-horizon policy pretraining.
- Curated by: Kled (useKled)
- Collected through: Phone camera (in app)
- Regions: Global, 100+ countries
- Volume: 50,000+ clips and stills (approx. 3 TB)
- Contributors: 6,200+
- Modality: Images + video
- Format: JPG / PNG / HEIC, MP4 / MOV
- Resolution: Native phone resolution for stills; video 720p to 4K as recorded
- Annotations: Raw (no labels)
- Quality control: 90% passed QA review
- License: Non-exclusive commercial training license (terms); exclusivity available on request
- Consent: Each contributor opted in to this use when they uploaded.
Dataset Sources
- Catalog page: kled.ai/datasets/organize-a-drawer
- Sample set: through the access request at the top of this page, or team@kled.ai
- Publisher: huggingface.co/useKled
Uses
Direct Use
- Long-horizon tidying policies
- Object grouping and placement prediction
- Before-and-after scene state
Out-of-Scope Use
Redistribution or resale of the files, use outside the terms of the commercial training license, and any attempt to identify or contact contributors or people who appear in the data. Uses that require labels this set does not carry should be scoped with the data team first.
Dataset Structure
Data Instances
One clip with its stills per record, delivered as JPG / PNG / HEIC, MP4 / MOV. Volume: 50,000+ clips and stills.
Data Fields
- File: the JPG / PNG / HEIC, MP4 / MOV file as captured or uploaded by the contributor
- Labels: none; the set is delivered raw. Labeling on the same items is available on request.
- Capture metadata: capture tool, country of capture, submission time
- Review status: QA outcome (90% passed QA review)
Field names and the manifest format are confirmed with the sample set.
Data Splits
Delivered as one set. Subsets by country, language or label, and custom splits, are available on request.
Dataset Creation
Curation Rationale
The dataset exists because the public alternatives named in the description are non-commercial, synthetic, small or collected elsewhere. Kled runs the task as a paid brief in its app so that the data is captured on purpose, under the capture rules above, by people who have opted in to commercial use.
Source Data
Data Collection and Processing
Contributors complete the task in the Kled app. Capture tool: Phone camera (in app). Each submission is reviewed against the task brief; submissions that break the capture rules are rejected. 90% passed QA review. Files are delivered as JPG / PNG / HEIC, MP4 / MOV, in the resolution noted above.
Who are the source data producers?
Kled contributors, who complete paid data tasks on their own phones. This task was open to contributors worldwide. Kled has 625,000+ contributors in 100+ countries, strongest in Southeast Asia, the United States and Europe.
Annotations
Annotation process
No annotations are included. Items are delivered as captured; labeling on the same items can be scoped with the data team.
Who are the annotators?
Not applicable; no annotations are included.
Personal and Sensitive Information
Every item was contributed by its owner, who opted in to this use at upload. The task brief states what may and may not appear in a submission, and reviewers reject submissions that break those rules. Kled's data handling practices are documented at trust.kled.ai; questions about handling requirements for your use go to team@kled.ai.
Bias, Risks, and Limitations
Coverage follows the contributor base: strongest in Southeast Asia, the United States and Europe, thinner elsewhere, and not limited by region for this task. Devices, settings and conditions vary by contributor, which is the point of the collection and also means no two items are captured under identical conditions. Where labels are contributor-entered, they are checked at review but are not expert annotations. Volumes are the current scope of the task and grow as collection continues.
Recommendations
Request a sample set and run it against your task before licensing. Ask for per-country or per-language counts where they matter to your use.
Licensing Information
Non-exclusive commercial training license; exclusivity on request. Each contributor opted in to this use when they uploaded. The license terms are on the catalog page.
Citation
@misc{kled_organize_a_drawer_2026,
title = {Drawer Reorganization Video},
author = {Kled},
year = {2026},
url = {https://www.kled.ai/datasets/organize-a-drawer},
note = {Commercially licensed dataset collected through the Kled app. Sample sets on request.}
}
Dataset Card Contact
Kled data team: team@kled.ai
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