Physical AI Training data
Physical AI training data, recorded on the real line.
A robot that routes cables or seats connectors needs examples from people who do exactly that every day. Taktil records skilled workers in live production, with force at the fingertips, the pose of each hand and video, and licenses it as training data for industrial robots.
Most robot data is recorded without touch.
Robot manipulation data mostly comes from three sources today. People steer a robot arm remotely and usually cannot feel what the gripper touches. Tasks are staged in a lab, at a table with a few objects. Or cameras film hands at work, in large volumes, without measuring force.
For assembly, all three miss the same thing: the moment a connector clicks home or a cable slips into its clip. On video you only see the hand stop. Whether the part is seated shows up in the force signal.
Physical AI, meaning AI that grips and acts in the real world through a robot, needs data with touch measured in it to learn tasks like these.
Four sources of robot data, side by side.
| Teleoperation | Staged in a lab | Video of hands | Capture in assembly | |
|---|---|---|---|---|
| Force | at the gripper, if sensors are fitted | depends on the setup | not measured | at the fingertips |
| Who leads the motion | a person through an input device | a person or a robot | a person with their own hand | a skilled worker with their own hand |
| Parts and setting | test cell | selected objects | depends on the source | real parts in live production |
The sources do not exclude each other, and many teams combine them. For tasks where force decides, Taktil supplies the part the others are missing.
What an assembly robot has to learn.
- 01How hard.A connector needs a firm push, a clip a short one, a thin wire hardly any. The force curve shows the difference.
- 02When exactly.Seating a connector takes a split second. Force, video and hand pose have to land on the same moment.
- 03With real parts.Cables bend, tolerances vary, and no harness lies on the board the same way twice. That variance only exists in live production.
- 04What goes wrong.A missed click, a stuck part, a second attempt. Moments like these teach a robot to correct itself.
Robot manipulation data from skilled work.
Skilled workers at partner plants volunteer to wear a capture kit during normal production. It records, all synchronized:
- 01Tactile and force signals at the fingertips
- 02Full hand and finger pose
- 03First-person and wrist video
- 04Action labels, time-aligned and quality-checked
Delivered in standard formats for robot learning. What such a dataset holds in detail is on the page Contact-rich manipulation dataset.
Applications: Wire harness assembly, Control cabinet wiring, Connector mating, electronics assembly and automotive final assembly.
You know where every dataset comes from.
- T.01Voluntary participation, with opt-out at any time
- T.02No performance monitoring of workers
- T.03Documentation designed for EU AI Act data governance (Article 10)
- T.04We license data. Exclusivity windows are available.
We scope capture programs around your tasks and your hardware.
Frequently asked.
Q.01What is physical AI?
AI that grips and acts in the real world through a robot. It learns from recordings of real movements, ideally including the force applied.
Q.02Do you build robots?
No. We are independent and supply data to robot makers and model teams.
Q.03Can we commission specific tasks?
Yes. We scope capture programs around your tasks and your hardware.
Q.04Is the data exclusive?
We license data. Exclusivity windows are available.
Q.05Does participation affect worker evaluation?
No. Data is never used to assess individual performance.
More on training data from assembly work.
Tell us which tasks your robot should learn.
Taktil does not build robots. We supply data to robot makers and AI model teams. We license data. Exclusivity windows are available.
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