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.
-
A service, not a dataset
A collection service produces data against your specification. What you are buying is the crew, the protocol and the quality control, not a fixed catalogue.
-
The protocol is the deliverable
The written procedure that makes episodes comparable is worth as much as the recordings themselves.
-
Outsourcing does not remove the specification work
A vendor still needs the task, environment and sensor decisions from you. That part cannot be delegated.
-
Most cost sits in setup
Crew time during recording is a smaller share of the bill than the preparation that makes recording possible.
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.
What belongs in the statement of work
A collection engagement goes wrong in predictable places. Every one of them can be closed off in the statement of work before anyone starts.
This list is not bureaucracy. Each item is a decision that has to be made by someone, and the only question is whether it is made deliberately or discovered later.
- Deliverable definition. What an episode is, what a batch is, and what the complete delivery contains.
- Acceptance criteria. The measurable standard a batch has to meet, and how it is checked.
- Sensor and hardware schedule. What is used, who owns it, and what happens if it fails mid-run.
- Environment control. Whether the site can be held constant, and what is recorded when it cannot.
- Operator qualification. Who is allowed to record, and how consistency between them is maintained.
- Metadata schema. The fields attached to every episode, agreed before capture rather than reconstructed afterwards.
- Change process. How a scope change is raised, priced and approved, and what happens to work already completed.
- Data handling. Where recordings are stored during the run, who can access them, and how they are transferred at the end.
A collection service is defined by what it can physically capture. These are the embodiments we source for, and the capture routes we can staff. Check your task against both before requesting a quote.
What can be collected
Grouped by the hardware the data comes off. The robot platform decides the action space, and an action space cannot be converted after the fact.
-
Robot Manipulation Data
Episodes of a robot arm moving objects, recorded together with the commands that produced the motion.
-
Grasping Data
Images, depth maps or point clouds of objects, annotated with the gripper poses that would pick each one up.
-
Dexterous Hand Data
Demonstrations for multi-fingered robot hands, including in-hand manipulation that a two-finger gripper cannot do.
-
Mobile Robot Navigation Data
Sensor logs of a robot moving through space, with the poses, maps and trajectories needed to train or evaluate navigation.
-
Humanoid Robot Data
Whole-body demonstrations and sensor logs from robots with legs and arms, where balance is part of every task.
-
Warehouse Robotics Data
Sensor and telemetry data from robots working in fulfillment centers, covering picking, moving totes and building pallets.
Scenario tags
How it is captured
The route decides both the price and the shape of the delivered data. Teleoperation and simulation produce the same task list in formats that are not interchangeable.
-
Teleoperation
A human drives the robot directly, and the robot's motion is recorded as a training demonstration.
-
Motion Capture Data
Human body motion recorded as joint trajectories, used for animation and as a reference for robots that imitate people.
-
Egocentric Video Data
First-person footage, usually from a head-mounted camera, showing hands and objects from the wearer's own viewpoint.
-
Sim-to-Real Transfer
Training a policy in simulation and getting it to work on physical hardware, across the gap between the two.
-
Robot Data Annotation
Attaching labels to robot episodes, recording what the task was, whether it succeeded, and when key events happened.
-
Robot Data Augmentation
Generating additional training episodes from existing ones by transforming images, goals or scene configurations.
Scenario tags
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. Datatoorder does that as a platform: we collect and process data in-house, trade it, and source to order: with no stock catalogue and 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 collect, process and trade what you actually need. Here is when each model makes sense.
-
Full data catalog
39 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.
- You send the spec Language, hours, recording conditions, and the deadline.
- We reply within two business days A real number and a real timeline, not a range.
- You approve a pilot batch A small run first, so problems surface before the full order.
- Full run, then delivery with documents Audio, transcripts, and the consent record for every speaker.