This separation has become more visible as AI systems grow more capable in digital tasks while still struggling with everyday physical reasoning. A model may generate complex text or analyze massive datasets, yet fail at tasks requiring balance, dexterity, or real-time adaptation. That gap has prompted researchers to reconsider how intelligence should be designed when it must act, not just compute.
Physical AI (PAI) narrows that gap. These systems are built with sensors and actuators that allow them to observe, interact with, and influence their environments. Instead of analyzing data from a distance, they operate within the conditions they respond to. In this model, intelligence develops through physical interaction with the world.
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What Do We Actually Mean by the Term 'Physical AI'?
To clarify what distinguishes PAI, it helps to compare it with the AI most people already know.
Traditional digital AI operates inside computer systems. It processes data, identifies patterns, and makes decisions through algorithms. Even when it controls real-world tools or infrastructure, its reasoning remains rooted in software.
PAI extends that model by tying intelligence to a physical body. Thinking, sensing, and movement are designed as parts of the same system. The objective is not only to analyze information, but to perceive the environment, respond to it, and adjust behavior in real time.
Researchers Miriyev and Kovac describe PAI as the integration of a robot’s body and its control system, where materials, sensing, actuation, and computation develop in coordination. In their view, intelligence depends on the relationship between physical structure and software rather than on code alone. As they note:
An appropriate balance between the brain and the body is a prerequisite for the creation of nature-like and fully integrated intelligent robots.
Miriyev & Kovac (2020)
That balance sits at the center of the concept. Intelligence does not emerge from code in isolation; it develops through the interaction between a system’s body, its sensors, its materials, and its control mechanisms.
A later study published in Frontiers of Information Technology & Electronic Engineering builds on this perspective, describing PAI as a multidisciplinary integration of autonomous robots, materials, structures, and perception, requiring deliberate coordination between hardware and software.2
In this framework, the body is not simply a container for intelligence. It shapes how information is gathered, interpreted, and acted upon. Sensors, joints, materials, and actuators all influence behavior. PAI treats intelligence as something that arises from continuous interaction between a machine’s structure and the world around it.
How Physical Intelligence is Structured
Once intelligence is tied to a body, design becomes inseparable from behavior. The materials selected, the placement of sensors, the configuration of joints, and the distribution of force all influence how a system learns and adapts.
Perception, in this context, depends on physical position and interaction. A pressure reading only has meaning relative to posture and applied force. A motion signal depends on balance and surface contact. Sensor data cannot be separated from the body that generates it.
Action feeds back into that same loop. When a robot shifts its weight or adjusts its grip, it receives immediate feedback from the environment. That feedback shapes the next movement. Over time, this continuous exchange between sensing and acting refines performance. Learning emerges through repeated engagement with physical conditions rather than abstract data processing alone.2,3
Once intelligence depends this heavily on embodiment and feedback, the question shifts from mechanism to scale: does it reside within a single system, or can it extend across multiple physical agents?
Two Scales of Physical Intelligence: Integrated and Distributed
Embodied intelligence can take two primary forms.
In Integrated Physical AI (IPAI), perception, computation, and action are contained within a single machine. A robot gathers data through its sensors, processes it locally, and responds through its mechanical components. Everything occurs within one physical unit. Home service robots, factory automation systems, medical devices, and autonomous vehicles fall into this category.
Distributed Physical AI (DPAI), by contrast, spreads sensing and decision-making across multiple connected units. Industrial Internet of Things (IIoT) systems provide a clear example. Sensors positioned across facilities collect data, local processors respond in real time, and decisions are coordinated across the network. Intelligence emerges through collaboration among nodes rather than from a single body.1,4
As systems grow more complex, from agriculture to transportation to healthcare infrastructure, distributed approaches become increasingly relevant. Whether concentrated within one machine or spread across many, the underlying principle remains the same in that intelligence is grounded in physical interaction.
The impact of that principle becomes clearer in practice.
Where Physical AI is Already Making an Impact
Physical AI appears wherever machines must respond to dynamic physical conditions.
In healthcare, robotic systems support patients with mobility impairments, assist in rehabilitation, and reduce strain on caregivers. A rehabilitation robot equipped with joint sensors can adjust the support it provides based on muscle resistance, breathing patterns, and movement. Instead of following a fixed program, it adapts to the individual.2,4
In manufacturing, hybrid systems combine physical modeling with machine learning to diagnose equipment faults without relying on labeled failure data. A zero-shot fault detection system for industrial gears, for example, can generate training data from randomized physical models and adapt it to real-world conditions. This allows faults to be identified even if a specific failure has never been observed during training.
Agriculture relies on coordinated sensing systems (cameras, temperature sensors, hygrometers) to monitor crop growth and estimate optimal harvest timing. In logistics, autonomous sorting robots and delivery drones address the persistent “last mile” problem, where physical delivery remains difficult to automate through software alone.2,4
Infrastructure introduces another set of physical constraints, particularly in environments that are unsafe or inaccessible to humans. In these settings, systems must tolerate heat, instability, debris, or extreme weather while continuing to function reliably.
Japan's Moonshot national project is one example of this direction. It supports the development of collaborative robots designed to operate in hazardous conditions, including disaster recovery sites and, in the longer term, potential lunar construction environments. The goal is to extend robotic capability into areas where safety risks or labor shortages limit human involvement.
FireDrone illustrates a similar principle at a smaller scale. This aerial robot is built with polyimide aerogel insulation and phase-change cooling materials, allowing it to operate in high-temperature environments that would disable standard electronics. In situations such as wildfires or industrial accidents, that resilience allows physical AI systems to continue functioning where conventional machines would fail.4
Across these domains, the pattern is consistent. When intelligence must account for force, variability, and environmental uncertainty, embodiment becomes essential.
At the same time, deploying physically intelligent systems introduces new constraints. Hardware durability, maintenance costs, safety validation, and regulatory oversight become as important as algorithm design. Scaling Physical AI is therefore not only a software challenge, but an engineering and infrastructure challenge as well.
Training Physical AI: From Risk to Simulation
One of the most significant challenges PAI faces is data.
Unlike digital AI, which learns from static datasets such as text or images, physically embodied systems learn through sequences of actions and observations. Every movement changes the environment, and those changes influence what the system experiences next.
Collecting this type of data in the real world can be slow, expensive, and potentially dangerous. Early-stage learning often involves trial and error. A robot exploring how to grasp an object or navigate a space may collide with surfaces, drop materials, or apply incorrect force. In controlled settings, those errors can be managed. In industrial or medical environments, the risks are considerably higher.
To reduce that risk, researchers have developed large-scale simulation environments. NVIDIA's Cosmos World Foundation Model platform, for example, creates a digital representation of physical environments in which systems can train safely. Instead of experimenting directly in the real world, a robot can test behaviors within a simulated setting that models physics, movement, and visual feedback.5
The platform follows a two-stage approach. A general model is first pre-trained on large volumes of video data representing real-world physical interactions. It is then fine-tuned with smaller, task-specific datasets focused on areas such as robotic manipulation or autonomous driving.
Training within these simulated world models allows developers to evaluate strategies, generate synthetic data, and test policies without exposing physical systems to damage. Even so, simulation cannot fully eliminate uncertainty. Real-world variability remains difficult to model perfectly, which makes physical validation essential.
Closing the Gap Between Simulation and Deployment
Simulation reduces risk during training, but it does not eliminate uncertainty. A persistent challenge in PAI is the gap between simulated environments and real-world deployment. Systems that perform well in controlled digital settings do not always behave the same way once deployed.
This “sim-to-real” gap exists because simulations can only approximate physical conditions. Factors such as surface friction, material fatigue, lighting variation, or unexpected obstacles are difficult to reproduce precisely. A driving policy trained in a simulator may struggle on actual roads where weather, tire wear, and subtle mechanical differences influence performance.
Researchers at Shanghai Jiao Tong University have addressed this issue through a technique by the name of latent space modeling. Rather than assuming simulated and real environments are equivalent, their framework analyzes differences beneath observable behavior.6
By mapping simulated and real observations into a shared latent space, the model can identify discrepancies and estimate how significant they are. This allows developers to assess how much simulated training can be trusted before real-world testing becomes necessary.
As embodied systems take on greater responsibility in transportation, healthcare, and infrastructure, that judgment becomes increasingly consequential. Small discrepancies between simulation and reality can carry material costs and safety risks.
Looking Ahead
As AI systems move further into the physical world, the practical limits of purely digital intelligence become harder to ignore. Operating under gravity, friction, heat, and mechanical wear changes what “intelligence” has to account for. In those conditions, materials, structure, and energy use matter just as much as algorithms.
Physical AI brings that reality into focus. It suggests that learning and adaptation are shaped not only by data, but by how a system is built and how it interacts with its surroundings. A machine’s behavior reflects its physical design as much as its model architecture.
That shift does not replace digital AI, but it does expand what intelligent systems require. Progress will depend on bringing physical engineering and computational design closer together. The more tightly those two elements are aligned, the more capable and reliable embodied systems are likely to become.
If Physical AI interests you, it may be worth exploring the articles linked below. These areas shape how intelligent systems move from theory into physical reality.
References and Further Reading
- Li, Y. et al. (2023). Physical artificial intelligence (PAI): the next-generation artificial intelligence. Frontiers of Information Technology & Electronic Engineering, 24, 1231–1238. DOI:10.1631/FITEE.2200675. https://link.springer.com/article/10.1631/FITEE.2200675
- Salehi, V. (2025). Fundamentals of Physical AI. Journal of Intelligent System of Systems Lifecycle Management. DOI:10.71015/z6mc6967. https://isl-journal.com/index.php/isl/article/view/49
- Jiang, J. et al. (2025). Embodied Intelligence: The Key to Unblocking Generalized Artificial Intelligence. ArXiv. DOI:10.48550/arXiv.2505.06897. https://arxiv.org/abs/2505.06897
- Dewi, R. S. et al. (2025). A Systematic Review of Physical Artificial Intelligence (Physical AI): Concepts, Applications, Challenges, and Future Directions. Journal of Artificial Intelligence and Engineering Applications (JAIEA), 4(3), 2246–2253. DOI:10.59934/jaiea.v4i3.1101. https://ioinformatic.org/index.php/JAIEA/article/view/1101
- Ming-Yu Liu. et al. (2025). Cosmos World Foundation Model Platform for Physical AI. NVIDIA. https://research.nvidia.com/publication/2025-01_cosmos-world-foundation-model-platform-physical-ai
- Lin, Z., & Sun, S. (2025). Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling. ArXiv. DOI:10.48550/arXiv.2506.12735. https://arxiv.org/abs/2506.12735
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