Glossary
What Is Physical AI?
Last reviewed: September 2026
Short answer
Physical AI is artificial intelligence that perceives and acts in the physical world through a body — a robot arm, mobile manipulator or humanoid — rather than only producing text or images. It is trained on data that describes how hands, objects and environments behave, including egocentric human demonstrations and robot trajectories.
01
Why it matters
Physical AI moves AI from screens into warehouses, factories and homes. Its hardest constraint is data: unlike text, there is no internet-scale archive of synchronized, labeled examples of people doing physical tasks. Programs typically move from assessing where a robot can create value, to piloting it in a real setting, to deploying it — and the pilot is where task-specific training data most often becomes the bottleneck.
02
How Firsthand handles it
Firsthand supplies the human side of physical AI training data: egocentric capture on a head-mounted global-shutter 4K camera with wrist cameras, stereo, depth and IMU, synchronized to a 1.12 ms median and 1.94 ms p99 residual. Collections are custom, defined by a written skill spec, and first validated data arrives in a median of 48 hours where coverage is deep. Data ships under a perpetual, buyer-owned license.
03
Physical AI program phases and their data needs
| Phase | Question it answers | Typical data need |
|---|---|---|
| Assess | Where can a robot create value? | Egocentric footage of the real job to decompose it into task units |
| Pilot | Can the robot do these tasks here? | Task-specific demonstrations in matched conditions, including failure cases |
| Deploy | Can it do them reliably at scale? | Ongoing targeted collection as new failures and tasks appear |
05
Frequently asked questions
- Is physical AI the same as embodied AI?
- The terms overlap heavily. Both describe AI that acts through a body; physical AI is often used in a commercial deployment context.
- What data trains physical AI?
- Web data, synthetic data, robot trajectories and egocentric human demonstration data, typically combined in a pretraining mixture and then specialized in later stages.
- Why is data the bottleneck for physical AI?
- There is no large archive of synchronized, labeled examples of people doing specific physical tasks, so much of it has to be captured.