RekaDaily-10k: 10,000 Hours of Egocentric Household Manipulation Data
Reka releases RekaDaily-10k: 10,312 hours of unscripted first-person household life video, recorded by a global paid collector network in real homes, with ~1,670 hours in native 4K. Released on Hugging Face under Apache 2.0 in both raw and processed+captioned tiers.
Why egocentric data matters
To understand and simulate the physical world, omni world models and vision-language-action models need more than text. They need high-quality visual data with descriptions of what is happening, recorded directly in the chaotic environments where real behavior occurs.
Search for cooking videos and you get an enormous library of edited, staged, tripod-mounted footage cut to keep the interesting parts. What a machine needs in order to learn a physical task is the opposite: one continuous first-person view of somebody actually doing it, at the speed they actually do it, in the mess they actually live in. Nobody uploads that, because nobody would watch it.
That footage has to be commissioned. Teleoperated data is precise but slow to produce, and it tends to inherit the tidiness of the space it was recorded in. Synthetic scenes scale but smooth over the clutter of real homes. Real first-person recordings sit in between, and the reason there are not more of them is that somebody has to pay people to make them.
Introducing RekaDaily-10k
To meet our own requirements for scale and quality, and to satisfy the needs of the broader industrial community, we built Claru, Reka's foundational data engine, which sources egocentric video through a global network of paid collectors.
Today we are releasing RekaDaily-10k: unscripted, first-person recordings of everyday household life, captured across homes by paid collectors recording their own routines, with a significant share in 4K. We are contributing this dataset to the research community as part of our open ecosystem initiative, under Apache 2.0, and the raw tier is available now on Hugging Face, with the full 10,312 hours live by early next week.
The dataset ships in two tiers:
- Raw tier. Unfiltered, raw footage, allowing teams to implement their own clipping, filtering, and annotation workflows. It has 10,312 hours.
- Processed and captioned tier. Footage that has been through our processing pipeline, cut into shorter clips and captioned, for teams that want language supervision out of the box.
The Apache 2.0 licence covers commercial use and redistribution. Each clip ships as video with a text caption. Roughly 1,670 hours are native 4K.
The Egocentric Landscape
The egocentric ecosystem has grown quickly, and this release is meant to add to it with more videos showing egocentric activities. Ego4D established the modality and remains the reference corpus for daily life. Egocentric-10K and the larger releases that followed it showed how far first-person data can scale, and they cover industrial work in real production environments. EPIC-KITCHENS is still the gold-standard of ego-centric benchmarks of human activities.
What RekaDaily-10k adds: Unscripted household activity recorded in real homes with detailed descriptions. Roughly 1,670 hours are in native 4K, which is a higher resolution than most large egocentric corpora. Together, this makes it an ideal complement for teams building domestic AI where language supervision matters.
Where The Footage Comes From
Claru is a paid collection network of more than 100,000 people recording the physical world. Collectors join a project, pass a qualification assessment, then record, submit, and get paid per accepted hour. The work spans domestic life, commercial environments and skilled trades across several regions. This release draws on the household portion of that network, recorded by a subset of those collectors on phones in head mounts.
Environment Diversity
Because every collector records in their own home, the number of distinct environments rises with the number of people who contribute rather than with the number of hours recorded. Different kitchens, appliance models, cabinet layouts, floor plans, lighting conditions and degrees of clutter. Different outlets and switch plates, different packaging on the shelves, different signage languages, different weather through the windows.
Quality Pipeline
Every submission passes through automated checks before a human reviewer sees it. The first pass flags technical problems: resolution too poor to make anything out, frozen or duplicated frames, wrong orientation, solid-colour padding, clips too short to use, footage that does not match the assigned activity. A second pass reads the content and asks whether it is what it claims to be, flagging non-POV, staged or screen-recorded video, and time inflation where somebody stalls to pad a per-hour payout. Every video also gets a perceptual fingerprint, because at this volume near-duplicates stop being hypothetical.
The quality thresholds came from data rather than instinct. We took a large sample of historical submissions that already carried a final human verdict and replayed them through the pipeline to compare machine flags against what reviewers had decided. Where the two disagreed we moved the thresholds toward the reviewers rather than toward numbers that look strict on paper. One principle is written into the code. A false rejection is the worst outcome, so borderline video goes to review instead of the bin, anomalous scores raise a warning rather than a rejection, and no automated check can reject a submission without clearing a confidence floor.
Captioning: Placing Activity in Context
Captioning is where the length of these recordings becomes the whole problem. A caption has to reflect where an activity sits in the arc of a session rather than what happened to be in one sampled frame, which matters when somebody loads the washer, wanders off, and comes back twenty minutes later to unload it.
A caption has to say where an activity sits in a session, not what happened to be in one sampled frame.
For the wider picture of how footage like this gets prepared for world model training, our data platform team wrote up the full pipeline in World Model Data Pipeline.
What You Can Train On It
Two things that can make this corpus useful.
The first is language tied to real activity. A caption on a stock clip describes a scene. A caption over a continuous session describes what a person was doing and in what order, with the pauses, mistakes and corrections still in frame. That is the supervision instruction-conditioned models run on, and the interesting part is not the captioning. It is having ten thousand hours of unscripted first-person footage worth captioning in the first place.
The second is coverage of ordinary domestic environments. If you are training a household robot, a world model, or a video generation model that has to respect how objects behave when handled, the distance between your training data and a real kitchen matters. This is thousands of real kitchens at the hours of day when people are actually in them.
The raw tier is deliberately unopinionated, and it is there because our processing choices should not be forced on you. We clip, filter and caption to serve the projects we run, and any team with its own pipeline will want different boundaries, different thresholds and its own annotation schema. Releasing the footage as collected means you can start upstream of every decision we made.
Get The Data
The dataset is on Hugging Face under Apache 2.0, ungated.
Following our June release of RekaCS2-10k, 10,000 hours of egocentric Counter-Strike 2 footage with per-frame action annotations and its accompanying open-source renderer, we are releasing RekaDaily-10k to advance progress in physical AI and foster an open research ecosystem.
Source: Reka Official Blog. Republished by RobotWorld with promotional content removed.
