Is physical AI the same as robotics AI?

Robotics is the main application of physical AI today, but the term also covers humanoids and other embodied agents that sense and act in the physical world rather than only processing text or static images.

What data does physical AI actually train on?

Synchronized first-person video and sensor streams from real tasks — camera feeds, depth, hand or end-effector pose, and action labels — captured with consent and delivered under a license that lets you train on it.

Can you collect physical AI data for a specific robot or skill?

Yes. A spec sets the skill, environment, and capture setup, then contributors perform the task on that spec. Bimanual manipulation and humanoid-specific programs are common starting points.

How is physical AI data priced and delivered?

Per validated hour or accepted item, quoted against your spec before capture. Delivery is in the format your training stack already reads — RLDS, LeRobot, WebDataset, HDF5, or JSONL — under a perpetual, buyer-owned license.

How fast can a physical AI dataset ship?

A median of a median of 48 hours from signed spec to first validated batch where coverage is deep, and three to four weeks where it is thin and contributors need to be recruited first.

Physical AI

Physical AI training data.

Physical AI is the umbrella term for models that sense and act in the physical world — robots, humanoids, and embodied agents. Training them takes egocentric, first-person data captured from inside real tasks, not text or web images. We collect it to spec, hardware-synchronized and consented, on demand.

Core data: First-person video + sensors
Sync: Single hardware clock
Formats: RLDS · LeRobot · WebDataset
License: Buyer-owned

A contributor wearing a head-mounted capture rig performs a manual task, beside a graphic of synchronized hand-pose, depth, and action-segment annotation layers. — Physical AI learns from the same first-person view a body uses to do the task — not a description of it.

Definition

What is physical AI training data?

Physical AI training data is the sensor and video data used to train models that act in the physical world — robots, humanoids, and other embodied agents. It typically means synchronized first-person (egocentric) video, depth, hand or end-effector pose, and action labels captured from real tasks, rather than text or web-scraped images, so a policy learns from the same viewpoint and physics it will operate in.

How it works

Why physical AI needs a different kind of data.

The viewpoint has to match the body

A robot or humanoid perceives from a head- or wrist-mounted camera, not a static overhead shot. Egocentric capture matches that viewpoint directly, so the policy trains on the same geometry it will see at inference.

Actions, not just images

Physical AI models predict what to do next. Data needs synchronized hand pose, contact events, and action-segment labels alongside the video, not a single classification tag per clip.

Timing has to be exact

When a policy fuses vision with depth, IMU, or proprioception, every stream has to share one clock. Software timestamps drift; the streams need to be hardware-triggered together.

It has to cover the real task distribution

Manipulation, locomotion, and human-robot interaction all fail differently. A physical AI program is scoped by skill and environment, not collected as one generic pile of video.

At a glance

What you get.

Capture: Head-mounted, wrist, or phone first-person
Sensors: RGB · depth · IMU · audio
Labels: Hand pose · action segments · contact
Spec to first batch: a median of 48 hours (deep coverage)
Delivery: RLDS · LeRobot · WebDataset · HDF5

Every collection runs through the same seven-stage end-to-end custom collection pipeline. End-to-end custom data collection is a managed service that takes an AI data need from problem to owned dataset in a single accountable pipeline.

FAQ