Physical AI Data
Physical AI is the umbrella term for systems that act in the world: robots, vehicles, drones, and the human demonstration data that teaches them. The choice that shapes the budget is machine-collected or human-collected.
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Real-world capture is the scarce part
Simulated and internet-sourced data are abundant. Data captured on real hardware in real environments is the constraint most physical AI programmes hit.
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Method decides the ceiling
Teleoperation, motion capture and autonomous logging produce different data with different costs. The choice is usually made once and lived with.
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Scale changes the protocol
A method that works for a hundred episodes often breaks at ten thousand, because operator consistency becomes the limiting factor.
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Provenance travels with the data
Where an episode came from, who operated it and under what conditions is part of the dataset, not an afterthought.
What the term covers
Physical AI is a newer label for an old requirement: training data in which the system perceives a physical situation, takes an action, and records what happened. Robotics and autonomous driving are the established cases; drones, industrial inspection, and wearable-captured human activity are the expanding ones.
The label matters less than what it implies for data. Every sample needs three things joined together. The sensor record, the action or state, and the outcome. Data that has only the sensor record, however high its quality, is not physical AI training data.
Machine-collected and human-collected
There are two families of collection, and the budget conversation goes better once the buyer picks one.
Machine-collected data comes off a platform: teleoperation episodes, autonomous runs, fleet logs. It carries true joint states and precise action records, and it costs operator and rig time. It is the right choice when the model has to control that platform.
Human-collected data comes off people: egocentric video from head or wrist cameras, motion capture, hand-pose tracking, wearables. It is cheaper per hour, scales with the number of participants rather than the number of robots, and is the raw material for imitation learning and for world models that predict how a scene evolves. It is the right choice when the target is general manipulation understanding rather than one machine.
Most serious programs end up buying both, because human data supplies breadth and machine data supplies control fidelity. Buying them under one specification is cheaper than running two projects, because the scene definitions and the annotation schema can be shared.
What drives cost and how long it takes
Sensor configuration is the first cost driver: more cameras mean more synchronization, more calibration, and more storage, and depth or tactile channels add their own processing. Annotation of interaction is the second: labeling what a hand is doing to an object is slower than labeling what the object is, and contact events, affordances, and failure moments are the labels that carry the value.
Human-collected data has a cost that machine data does not: the people in the frame. Participants have to be recruited, briefed, and consented, and bystanders who wander into egocentric footage have to be excluded or blurred by a rule that is documented and applied consistently.
A pilot is possible within weeks once the sensor setup is fixed. Production then scales with participants and scene count rather than with studio time, so a human-collected project of a few hundred hours can finish faster than an equivalent robot project: recruitment is the pacing item.
What you receive and what to settle first
Deliverables follow the family of data. Machine-collected projects deliver episodes with aligned streams and per-session calibration records. Human-collected projects deliver episodes with camera calibration where relevant, participant consent documentation, and the blurring or exclusion rules that were applied. Both come with the annotation schema and a QA report covering what was checked and what was rejected.
Before production, the buyer settles four things: which family the project needs, the sensor or capture configuration, the task and object taxonomy the annotation will follow, and the rights position for the people recorded. The last one is the one buyers most often leave late, and the one that cannot be fixed after capture.
One boundary to state early, because physical AI work runs close to it: we do not take on medical or clinical data. Surgical and clinical capture is out of scope here even when the platform is a robot, and recorded telephone calls are declined for the same reason they are everywhere else. The other party never consented.
Capture methods compared
| Dimension | Typical trade-offs | Where Datatoorder fits |
|---|---|---|
| Teleoperation | High-quality action data with a human in the loop; slow and expensive per hour | We can organise operator pools and run teleoperation against a defined task list |
| Motion capture | Very accurate joint trajectories; needs a fixed rig and limits the environment | Useful where the task can be brought to the rig rather than the rig to the task |
| Autonomous logging | Cheap per hour and scales well; labels are weak and failures are under-represented | Best combined with a smaller teleoperated set for the hard cases |
| Simulation | Unlimited volume and perfect labels; the gap to reality is the whole problem | We do not produce it, and it does not replace real capture |
| Manual labelling of video | Low equipment cost; gives no action or force data at all | Applies to the vision side of a programme, not the control side |
The honest summary: simulation is cheap and real capture is not, and no amount of simulation removes the need for real episodes in the final training mix.
There is no single best method. Each produces a different kind of data at a different cost, and the right choice depends on what the model has to learn.
The comparison below is about what each method is good at, not about which is superior. Most programmes end up combining two of them.
Physical AI spans more than robot arms. These are the sources the data comes from and the applications it ends up in. If your project sits anywhere on this page, it is in scope.
Where the data comes from
Open corpora, human demonstration, and simulation. Most production projects combine at least two of the three.
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DROID Dataset
A large in-the-wild robot manipulation dataset collected by a multi-lab consortium, notable for scene diversity rather than task repetition.
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Open X-Embodiment
A pooled collection of robot datasets spanning 22 different robot embodiments, built to test whether data from one robot helps another.
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AgiBot World
A large real-world robot dataset collected on a standardized humanoid platform, notable for being recorded at production scale by one organization.
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Teleoperation
A human drives the robot directly, and the robot's motion is recorded as a training demonstration.
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Motion Capture Data
Human body motion recorded as joint trajectories, used for animation and as a reference for robots that imitate people.
Scenario tags
What it is used for
The application decides the sensor set and the annotation schema. Two projects with the same task list but different deployment targets need different data.
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Vision-Language-Action Model
A model that takes camera images and a written or spoken instruction and outputs robot actions directly.
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Robot Manipulation Data
Episodes of a robot arm moving objects, recorded together with the commands that produced the motion.
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Mobile Robot Navigation Data
Sensor logs of a robot moving through space, with the poses, maps and trajectories needed to train or evaluate navigation.
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Humanoid Robot Data
Whole-body demonstrations and sensor logs from robots with legs and arms, where balance is part of every task.
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Warehouse Robotics Data
Sensor and telemetry data from robots working in fulfillment centers, covering picking, moving totes and building pallets.
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World Model
A learned model that predicts how a scene will change in response to actions, used both for planning and for generating training data.
Scenario tags
Questions we get asked
Is physical AI data the same as robotics data?
Mostly, but the term is broader. Robotics data is machine-collected by definition. Physical AI data also includes human demonstration and human activity capture, which is where much of the growth is.
Can you annotate data we collect ourselves?
Yes. If your team or fleet already captures the raw material, we can scope the annotation, schema, and QA against your specification, which is often cheaper than collecting again.
Do you hold physical AI datasets in stock?
No. Nothing on this site is inventory. A project is specified, matched to a provider with the right rig or capture network, and produced against your order.
Keep reading
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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.
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AI Training Data Providers
What to check before you sign with a training data provider, and how we compare on each point.
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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.
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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.