What is physical AI?
A plain-English guide to AI that senses, decides and acts beyond a screen.
Three parts. One loop.
Sense
Cameras, microphones and other sensors provide information about the surrounding world. What the machine detects depends on its hardware, software and operating conditions.
Decide
Software interprets that information and selects an action. Some systems combine learned models with tightly defined rules and controls.
Act
A motor moves, a gripper closes or a machine changes course. The result becomes new information, and the loop continues.
A person steps into the route.
What happens next?
Imagine a supervised robot carrying an item through a designated indoor area.
- Sense
Notice the obstacle.
Sensors provide information about a person ahead and the surrounding space.
- Decide
Choose within the rules.
The system checks its permitted actions. In this example, it selects a pause.
- Act
Stop. Sense again.
The robot stops moving and reassesses. A person can intervene if it cannot continue.
Conceptual example, not a demonstration of a particular robot. Actual behaviour must be verified for the system and environment.
Why a body changes things.
A digital mistake can produce a poor answer. A physical mistake can damage an object, interrupt a workflow or affect a person nearby. Testing therefore needs to include the real environment, recovery steps and human oversight.
Capability also depends on much more than the model: battery life, reach, grip, maintenance and access all shape what is practical.
Start with the task
Describe the action and the conditions it must work in. “Move these objects between these two points” is more useful than “use AI”.
Define the boundary
Specify where the system can operate, who supervises it and what happens when something changes.
Measure the result
Agree on observable success: task completion, intervention rate, experience quality and total operating effort.
Is every robot physical AI?
Definitions vary. A robot following a fixed sequence is different from one using learned models to interpret a changing environment. Ask suppliers which decisions are adaptive, which are pre-programmed and which require a remote human operator.
Explore the technologies ↗An Australian example.
In March 2026, CSIRO reported trials using autonomous robots and sensors to inspect solar farms and identify maintenance issues. It is a concrete example of sensing and movement being evaluated against an operational task.
The useful question is what the trial needs to prove: reliable inspection, workable coverage and actionable information for the people maintaining the site.
Read CSIRO’s report on the solar-farm trials ↗External research example. Akshar Technologies was not involved in this trial.
Read further.
NVIDIA describes physical AI in terms of systems that perceive, reason about and act in the physical world. Its physical AI explainer provides a technology vendor’s introduction to the field.
For the Australian context, the National Robotics Strategy covers national capability, adoption, responsible use and skills.
CSIRO’s human–robot interaction research explores collaboration, interfaces and human-centred evaluation. These are external resources; they do not imply an Akshar Technologies partnership.
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