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Physical AI describes AI systems that sense and act in the physical world. It can power robots, autonomous vehicles, and other systems that respond to real environments. Traditional robotics is the broader field of designing and operating robots; many robots rely on rules and routines programmed by people. The two are not opposites: a single robot can combine learned AI with conventional control.
What does “physical AI” mean?
Physical AI is a broad label for systems that use perception and decision-making to interact with physical environments. A system may combine cameras or other sensors, learned models, planning software, and motors or other actuators. It senses what is happening, selects a response, and acts; feedback from the environment can then inform what it does next.
NVIDIA frames physical AI as extending generative AI with spatial relationships and physical behavior. In that framing, multimodal inputs such as images, video, text, speech, and sensor data can be used to produce insights or executable actions. That is a vendor’s description; the practical distinction is that physical AI involves acting in, or responding to, the physical world. See NVIDIA’s explanation of physical AI.
The related term embodied AI also refers to AI connected to an embodied system. The terminology overlaps, and there is no single universally accepted boundary between the two labels. An ITU-T recommendation published in December 2025 describes a framework for embodied AI systems, but it should not be read as standardizing every use of “physical AI.” ITU-T Recommendation F.748.66.
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How physical AI differs from traditional robotics
Robotics is an engineering domain: it covers the design, construction, control, and operation of robots. Physical AI emphasizes how an intelligent system perceives its surroundings, makes decisions, learns, and acts in them. A robot can use physical AI, but physical AI can also describe systems beyond robots, such as autonomous vehicles or smart spaces.
A useful contrast is between fixed, human-authored routines and behavior shaped by learned models. Deloitte’s 2025 report describes pick-and-place robots and automated guided vehicles as examples of conventional machines executing pre-programmed, rule-based instructions. It contrasts these with physical-AI approaches that may use neural networks, including vision-language-action (VLA) models that process visual inputs, interpret language commands, and produce physical actions. This is a comparison of approaches, not a rule that every conventional robot is inflexible or every physical-AI system uses a VLA model. Deloitte, Robotics and Physical AI (2025).
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| Comparison point | A conventional, rule-based approach | A physical-AI approach |
|---|---|---|
| Control | People author rules, sequences, or routines for a defined task. | A learned policy or model may help choose actions from current inputs; conventional rules may still constrain it. |
| Inputs | May rely on known object states or a fixed set of sensor readings. | May combine sensor data with visual, language, or other inputs. |
| Response to change | Behavior depends on how the programmed routine handles a changed situation. | May adapt to variations such as object pose or layout, but adaptation must be demonstrated for the task and conditions. |
| Evidence to look for | Whether the routine performs the defined task reliably. | Whether it has been validated on real hardware in conditions like those it will face, not only in simulation. |
These are comparison criteria, not a universal scoring system. Real systems can mix approaches: learned perception or action selection may operate alongside programmed limits, fallback routines, and human supervision.
What physical AI can do
Examples described in NVIDIA’s materials show how physical AI may be applied, without implying that every deployment is fully autonomous:
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- Warehouse mobile robots: navigate around people and other obstacles.
- Robot manipulators: adjust a grasp’s position or strength to suit an object’s pose.
- Autonomous vehicles: interpret sensor data to understand surroundings and guide movement.
- Warehouse and factory systems: use computer vision to support activity monitoring or route planning.
These examples illustrate a range of sensing and action. They do not establish that a system can handle every unexpected event without oversight.
How simulation-to-real robot learning works
A common development loop uses real or synthetic data, a physically based simulation, policy training and evaluation, and deployment to real hardware. Simulation makes it easier to vary conditions such as lighting, object positions, and scenarios, and to explore some failures without damaging equipment. But success in a simulator does not prove safe or reliable operation in the physical world.
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Example: NVIDIA’s SO-101 arm course
NVIDIA’s SO-101 course describes training and deploying a robot arm, from simulation to a physical robot, for an unstructured centrifuge-vial pick-and-place task. The course identifies the “sim-to-real gap”—differences between simulated and real-world behavior—as a fundamental challenge and describes systematic ways to reduce it. It also explicitly characterizes the SO-101 as a learning platform, not a production robot. Read the SO-101 course overview.
For a real-world assessment, look beyond a simulation demo. Check whether the system was tested on hardware, whether test conditions resemble the intended environment, how it behaves when inputs or objects vary, and what supervision or fallback behavior is available.
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How to evaluate a physical-AI claim
The label alone does not tell you how capable, safe, or autonomous a product is. When comparing a conventional robot with a physical-AI system, ask:
- What is the control approach? Is the behavior based on authored rules, learned policies, or a hybrid?
- What inputs does it use? Does it depend on fixed sensor readings, or does it interpret richer inputs such as images and language?
- What changes can it handle? Ask for evidence involving new object poses, layouts, lighting, or unexpected events—not just a successful demonstration under one setup.
- Has it transferred to real hardware? Simulation results are useful, but they are not substitutes for validation in conditions resembling the intended task.
- What are the safety boundaries? Identify human oversight, operating limits, failure handling, and fallback behavior.
What market figures do—and do not—show
Deloitte’s 2025 report forecasts that the addressable market for humanoids could reach US$38 billion by 2035. The same report says robotics startups raised over US$7 billion in seed-stage through growth-stage investments during 2024. These figures provide context about humanoids and robotics investment; they are not measures of physical-AI adoption specifically, and the market figure is a forecast rather than an established outcome. Deloitte’s 2025 report.
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