Robot Data Collection Services
Buying robot data collection as a service means renting someone else's rig, operators, and scenes. How to judge a provider, and what the contract has to settle before work starts.
What a collection service actually provides
A robot data collection service is not a robot and a room. It is a platform that is maintained and calibrated, operators who can drive it well, a scene inventory that can be reset to a defined state, and the process discipline to keep all of that consistent across weeks of sessions. When a provider quotes per episode, that price contains all of it.
The capability that separates providers is reproducibility. Anyone can record a demonstration. What you are paying for is the ability to record the same task again next week, in a different scene, with the same sensor configuration, and have the two batches fuse into one usable dataset.
In-house or outsourced
The honest case for collecting in-house is control. You own the platform, you control the environment, and research that depends on tight iteration between collection and training is faster when both happen in the same building. The cost is that you pay for the rig, the space, and the operators whether or not you are collecting.
The case for a service is that you pay per delivered episode and you rent scene diversity instead of building it. If the specification needs forty environments, a provider with an existing scene inventory delivers that in weeks; building forty scenes yourself takes months and is a facilities project, not a data project.
The split that works for many teams is to keep iterative research collection in-house and put the volume run — the part that is well specified and repetitive — with a service.
What to check before you sign
These are the questions that separate providers who have run real robotics projects from providers who have run a demo.
- Which platforms do they run, exactly? A provider with one arm model cannot serve a project specified on a different platform, because joint conventions and action spaces differ.
- Who are the operators, and how were they screened? Teleoperation quality is a skill, and it is the most common hidden variable in delivery quality.
- What is the calibration procedure, and what gets recorded? If intrinsic and extrinsic camera parameters are not captured per session, multi-view data cannot be fused later.
- What safety and insurance cover exists? Robots working near people, and props that break, are real risks, and the contract should say who carries them.
- What is the re-work policy when a batch fails QA? A provider with a real quality process will have an answer; one without will change the subject.
How the engagement is structured
A service engagement is milestone-based. Specification and feasibility first — this is where an honest provider tells you that the platform is unavailable or that the scene inventory does not cover what you asked for. Then a pilot batch with defined acceptance criteria. Then production, invoiced against delivered and accepted episodes rather than against calendar time. Then QA and packaging.
A typical mid-size engagement runs eight to twelve weeks end to end: the first weeks go to specification, feasibility, and the pilot; production is measured in weeks after that; QA and packaging take one to two more. Uncommon platforms stretch the front of that schedule, because the hardware has to be located before anything can be quoted honestly.
Deliverables follow the same list as any embodied project: episodes with aligned sensor streams, a calibration record for each session, the annotation schema the data was labeled under, and a QA report covering what was checked and what was excluded. The buyer's decisions do not change from an in-house project either — platform, task list, sensor configuration, success criteria, and annotation depth all freeze before the pilot. What changes is that hardware risk and operator recruitment move to the provider, and the price reflects that.
We do not hold robots or facilities of our own. A request goes to providers that have the platform and the scenes the specification needs, and if none of them can meet it, the answer is no rather than a quote that quietly weakens the specification.
Questions we get asked
Can you match a specific robot platform?
That depends on what is available in the market at the time. Common research platforms have several providers; uncommon or proprietary platforms often have one or none. We say which case yours is before quoting.
Is field collection different from lab collection?
Yes, and the price difference is large. A lab or studio environment is controlled and resettable. Field collection in real settings — kitchens, warehouses, outdoors — adds travel, safety planning, and setup time, and it usually cannot be reshot.
Who owns the data and the hardware risk?
Data rights are set in the agreement and pass to the buyer. Hardware risk sits with whoever operates the platform, which is normally the provider during production; that is one of the things a service contract is for.
Keep reading
-
AI Data Brokerage
A data broker sits between the people who need training data and the people who can produce it. We source to order — no inventory, no license resale, no recycled datasets.
-
AI Training Data Providers
What to check before you sign with a training data provider — and how we compare on each point.
-
AI Training Data Marketplace
Marketplaces sell what already exists. We source what you actually need. Here is when each model makes sense.
-
Full data catalog
36 data categories across 120 languages, and how to specify each one.
Submit a sourcing request
Tell us the language, the hours, and what the data needs to look like. You will get a real number and a real timeline — not a range. If we cannot source it well, we will tell you that instead.
- Pilot batch before the full run, so problems surface early.
- Consent documentation delivered with the data.
- No medical or clinical data. No recorded telephone calls.