Physical AI vs Robotics

Describe the whole system first

A robot can include mechanics, actuation, sensing, controllers, communications, software and tooling. Physical AI is a useful label for learned capabilities within or around that system. This guide does not divide every robot into an AI or non-AI box. Instead, identify where learning is used and how those components connect to the machine. A sophisticated model does not specify the rest of the equipment.

Separate flexible behavior from process engineering

A learned policy may be relevant when object appearance or task instructions vary. The surrounding process still needs defined inputs, fixtures, handoff points and output checks. Universal Robots’ distinction between an arm and an application solution illustrates why comparing manipulators alone can miss much of the installed system. Ask which variation the model handles and which variation the cell design removes.

Locate responsibility for each interface

A procurement diagram should assign responsibility for the gripper, camera calibration, motion interface, task software, fleet connection and recovery. ROS 2 is one software ecosystem used in robotics, but naming it does not prove a complete integration. Request the actual interfaces and versions, plus who maintains them when the model or robot firmware changes. Integration ownership is part of the product decision.

Compare against the required process

Do not assume that adding a foundation model improves every automation project. Define the variation and failures that matter, then evaluate candidate systems against that workload. A repeatable fixed process and a changing handling task may justify different solutions. Use the same acceptance conditions and labor accounting for each candidate; neither an AI label nor an established robot brand should decide the outcome in advance.

Ask what remains after the demonstration

Request the installation scope, tooling, software access, training, maintenance and change procedure. For a research checkpoint such as OpenVLA, identify the work required to build an operational controller. For a packaged robot cell, identify the boundaries of the application package. The objective is a complete responsibility map and testable workflow, rather than a contest between robotics and AI terminology.

Additional disclosed fields

Hardware/control example

FANUC supplies robots, CNC systems and other factory equipment

Primary specReviewed 2026-09-28

Sources 1

FANUC — official portfolio and company information

FANUC · Publication date not disclosed · Source accessed 2026-09-28

Official page read during live research; confirms identity and listed offering. Performance language remains a company claim.

Application-integration example

Universal Robots separates arms, controls and UR+ application solutions

Primary specReviewed 2026-09-28

Sources 1

Universal Robots — official portfolio and company information

Universal Robots · Publication date not disclosed · Source accessed 2026-09-28

Official page read during live research; confirms identity and listed offering. Performance language remains a company claim.

Learned-control example

OpenVLA provides research checkpoints and adaptation tooling

Primary specReviewed 2026-09-28

Sources 1

openvla/openvla official repository

openvla · Publication date not disclosed · Source accessed 2026-09-28

Official README inspected live. Repository code terms do not automatically cover model weights, data, or third-party assets.

Relationships & deployments

Related reading is an editorial crosslink. Sourced connections describe relationships reported in the cited material. A link to a versioned profile does not establish compatibility with that version unless the connection note explicitly identifies it.

Record history & verified changes

A research review records when we checked a source. It does not mark a product launch or a new deployment.

Initial reviewed record. No subsequent field change has been recorded.