Physical AI

Robots that navigate a warehouse on their own, agricultural machines that recognize their environment, and inspection robots that flag anomalies themselves. Machines are becoming increasingly capable not only of gathering information, but also of responding independently to what happens around them.

This development is called Physical AI. With Physical AI, artificial intelligence is applied in machines that operate in the physical world. They use sensors to perceive their environment, process this information, and can then carry out a physical action themselves.

For the IoT market, this development is particularly relevant because an increasing number of connected machines not only collect data, but also independently interpret their environment, make decisions, and act.

But when exactly do we speak of Physical AI? Is every smart robot a form of Physical AI? And what is the difference with traditional robotics, Embodied AI, and generative AI? This knowledge base article explains the basics.

What is Physical AI?

Physical AI is artificial intelligence that enables a system to perceive the physical world, make decisions, and act on them.

This usually happens in three steps:

1. Perceive: sensors gather information about the environment.
2. Decide: software and AI models interpret this information and determine what needs to happen.
3. Act: the machine then carries out a physical action.

Consider, for example, an autonomous warehouse robot that detects a person on its route. The sensors recognize the obstacle, the system determines that the current route is not safe, and the robot slows down or chooses a different route.

The difference with many other forms of AI is therefore mainly that the outcome does not remain purely digital. The system actually influences the physical environment.

How does Physical AI work?

A Physical AI system usually consists of several components that work together.

Sensors perceive the environment

A machine must first be able to understand what is happening around it. Various types of sensors can be used for this, such as:

• cameras
• LiDAR
• radar
• ultrasonic sensors
• GPS
• temperature sensors
• pressure sensors

Which sensors are needed depends entirely on the application. An autonomous agricultural machine, for example, needs different information than a robotic arm in a factory.

AI interprets the information

The collected data is then processed by software and AI models. A camera system can, for example, recognize that a person is standing in front of a vehicle, while an agricultural robot can use image recognition to distinguish between a crop and a weed. The system uses this information to determine what should happen next.

The machine carries out an action

The final step is the physical action. For a robot, this can mean, for example, that it:

• stops
• accelerates
• chooses a different route
• picks up an object
• sorts a product
• inspects a component
• treats a crop

It is precisely this combination of perceiving, deciding, and physically acting that characterizes Physical AI.

What is the difference between Physical AI and traditional robotics?

Robots have of course existed for much longer than Physical AI. A traditional industrial robotic arm can, for example, perform the exact same movement thousands of times a day. That makes the robot automated, but not automatically intelligent. The key difference lies in the ability to deal with change.

A traditional robotic system often works according to predefined instructions: pick up part A and place it at position B. When the environment changes, the system often has to be reprogrammed or reconfigured.

A Physical AI system, by contrast, can use information from its environment to adjust its behavior. If part A is positioned a few centimeters differently, for example, a robot equipped with cameras and AI may be able to determine the part’s location itself and adjust its movement accordingly. This does not mean, however, that every modern robot is automatically Physical AI. Many robots still largely work according to fixed rules or programmed processes.

What is the difference between Physical AI and generative AI?

Generative AI and Physical AI both make use of artificial intelligence, but are used for different tasks. Generative AI is mainly known for systems that produce new digital output, such as:

• text
• images
• audio
• video

Physical AI, on the other hand, uses AI to be able to function in a physical environment. A chatbot can, for example, describe a route for a warehouse robot. A Physical AI system must actually be able to drive that route, recognize obstacles along the way, and respond when the situation changes.

The two forms of AI can, incidentally, also come together. Generative AI can, for example, be used to interpret instructions, while other AI models are responsible for perception, navigation, and physical actions.

Is Physical AI the same as Embodied AI?

The terms Physical AI and Embodied AI are closely related and are, in practice, regularly used alongside or interchangeably with one another.

Embodied AI mainly emphasizes the idea that intelligence resides in a physical system that can learn or act through interaction with its environment. Physical AI is generally used more broadly for AI systems that link intelligence to physical machines, robots, and autonomous systems. There is no fully fixed dividing line between the two terms. In many applications there is considerable overlap.

For organizations, it is therefore usually more relevant to look at what the system can actually do:

• can it perceive its environment?
• can it make decisions independently?
• can it adapt its behavior?
• can it then act physically?

Examples of Physical AI

Physical AI is not limited to humanoid robots. The technology can be applied in many different types of machines and vehicles.

Logistics and warehouses

Autonomous Mobile Robots, often abbreviated to AMRs, navigate warehouses and distribution centers independently. They can transport goods, adjust routes, and respond to other vehicles or employees moving through the same environment. Autonomous forklifts and other vehicles for internal transport are also increasingly part of this development.

Autonomous vehicles

Self-driving cars, shuttles, trucks, and vehicles on closed industrial sites use sensors and software to continuously assess their environment. The system must, for example, recognize other vehicles, road markings, people, and obstacles, and respond accordingly.

Agriculture

Machines are also becoming increasingly autonomous in agriculture. Examples include:

• autonomous tractors
• field robots
• harvesting robots
• robots that recognize weeds
• autonomous mowers

A field robot can, for example, use cameras to recognize where crops are located and adjust its work accordingly.

Industry

In factories, robots are becoming increasingly capable of dealing with variation. Vision systems can, for example, determine how a randomly placed part should be picked up, after which a robotic arm adjusts its movement accordingly.

Inspection and maintenance

Robots and drones can be used to inspect, for example:

• railways
• electricity networks
• wind turbines
• pipelines
• industrial installations

AI models can help automatically recognize irregularities or damage in the process.

Public spaces

Autonomous applications are also increasingly emerging outside industrial environments. Examples are autonomous:

• mowers
• street-cleaning machines
• waste collection vehicles
• snowplows

Physical AI is therefore not limited to a single sector. The technology can play a role in virtually any environment in which a machine needs to be able to respond independently to change.

Why is Physical AI receiving so much attention now?

Much of the technology needed for Physical AI has existed for some time. However, the combination of these technologies is becoming increasingly powerful.

Sensors have become smaller and more affordable, computers can process increasing amounts of information locally, and AI models have become better at, among other things, image recognition, navigation, and interpreting complex environments. As a result, machines can carry out tasks that were previously difficult to fully automate.

A robot, for example, no longer has to function exclusively in a fully controlled environment. An increasing number of systems can deal with people, moving objects, weather conditions, and other changes.

As a result, automation is gradually shifting from fixed processes to systems that can adapt to the situation they find themselves in.

What role does IoT play within Physical AI?

Physical AI and IoT are not the same. IoT is fundamentally about connecting devices and machines so that data can be collected, exchanged, and managed. Physical AI is about machines that can independently perceive, decide, and act based on information.

The two technologies, however, increasingly come together. An autonomous robot can, for example, determine locally how it navigates around an obstacle, while at the same time data is sent to a central platform via an IoT connection.

Think of, for example:

• location
• battery status
• error messages
• sensor data
• usage data
• maintenance information
• software and model updates

The connection therefore does not necessarily have to be responsible for every real-time decision made by the machine. Many critical decisions are in fact made locally on the device or at the edge.

Connectivity becomes especially important when machines need to be managed or monitored remotely, or need to communicate with other systems.

From one autonomous machine to an entire fleet

With a single robot at a single location, the connectivity question is relatively straightforward. That changes once an organization uses dozens, hundreds, or thousands of machines across different locations or in multiple countries.

New questions then arise: how are all machines monitored centrally? How are software updates carried out? How is a malfunction investigated remotely? What happens when a mobile network at a location has poor coverage? And how are devices managed as they move between countries?

At that point, connectivity becomes part of a broader infrastructure surrounding Physical AI.

Does every Physical AI application need internet access?

No. A Physical AI system does not need to be continuously connected to the internet to function. Many essential tasks are carried out locally. An autonomous robot, for example, should not be dependent on an external cloud connection to be able to stop at the last moment for a person.

A connection to external systems can, however, be necessary for, for example, monitoring, fleet management, software updates, diagnostics, or data exchange. How important connectivity is therefore differs strongly per application. An industrial robotic arm that always stands in the same factory has different requirements than an autonomous agricultural machine that moves across large areas every day.

Is every autonomous robot Physical AI?

Not automatically. A system can be autonomous without using extensive artificial intelligence. A machine that only follows predefined routes and rules can, for example, function independently, but does not necessarily have to be a Physical AI system for that reason.

The more a system uses AI to interpret its environment and adapt its behavior accordingly, the closer it comes to what is now understood as Physical AI.

What does Physical AI mean for the future of machines?

Machines are becoming less and less dependent on fully predictable environments. Whereas traditional automation mainly works well when processes and conditions are known in advance, Physical AI makes it possible to deploy machines in more dynamic environments.

This does not mean that every machine will become fully autonomous. In practice, different forms will continue to exist alongside one another: fixed automation, remotely operated machines, partially autonomous systems, and machines that function to a large extent independently.

Physical AI mainly represents a next step in the way in which software, AI, and machines work together with the physical world.

Conclusion

Physical AI brings artificial intelligence and physical systems together. As a result, machines can perceive their environment, process information, and act independently based on that.

The technology is applied in, among other things, robotics, autonomous vehicles, agriculture, logistics, and industrial environments. It is important, in doing so, to distinguish between the intelligence of the machine itself and the systems needed to manage and connect these machines on a larger scale.

IoT connectivity mainly plays a role in applications such as monitoring, fleet management, software updates, diagnostics, and communication with central systems. As Physical AI applications are rolled out across more locations, machines, and countries, this infrastructure becomes increasingly important.

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