[Editor's Note] While conventional AI focuses on inference and generation within digital data, Physical AI directly acts and reacts in the real world through sensors, edge computing, robots, and control systems. The implementation of Physical AI can significantly advance industrial innovation and automation because it directly acts and solves problems in the real world, and it directly interacts with the real world. Accordingly, global companies such as Nvidia, Tesla, and Google are making massive investments in Physical AI, and the related market is expected to grow explosively. To implement this Physical AI, recognition technologies such as sensors, as well as edge computing and embedded systems for real-time data processing, local computing, robotics, and control technologies are essential. Accordingly, e4ds News has prepared a series of articles to examine the core technologies and implementation strategies of physical AI, including the concept, market outlook, related technologies, and actual cases.


Nvidia CEO Jensen Huang said in his CES 2025 keynote speech that “the next frontier of AI is physical AI.”

'Physical AI' is a concept fundamentally different from existing robotics or automation technologies.

According to the definitions of the World Economic Forum (WEF) and the Software Policy and Research Institute (SPRi), physical AI is an intelligent system that perceives the real world like a human, makes autonomous judgments and actions, and interacts organically with the environment.

This means that intelligence has evolved beyond simply installing AI in machines to a stage where it takes physical form and moves reality.

According to experts, physical AI is composed of four core components: brain, senses, neural networks, and behavior.

Brain (Cognitive Control) refers to an AI-based foundation model that understands situations and plans tasks, while Perception refers to recognizing the environment with computer vision, LiDAR, and tactile sensors.

Neural networks (Connectivity) are about ensuring real-time responsiveness through edge computing and on-device AI, and behavior (Mobility) is a control system that executes physical movements based on AI judgment.

As these elements converge, physical AI will go beyond simple automation to become capable of collaborating with humans and actively responding to unpredictable situations.

The emergence of physical AI divides the evolution of robotics technology into three stages.

First, standardized tasks are automated using rules. It means performing something repeatedly. For example, a welding robot on an automobile assembly line.

Next, training-based automation learns through simulations or real-world data, such as parts set-up work in logistics centers.

Finally, context-based physical AI can be considered as autonomous driving and collaboration with humans by autonomously responding to new environments through zero-shot learning.

These three are complementary, not interchangeable, and physical AI expands existing automation toolkits to enable intelligence in areas previously impossible.

Physical AI is not a single technology, but a complex system organically connected from hardware to application software. The World Economic Forum analyzes this into five layers: applications, simulation/training, operating systems, edge hardware, and robot hardware.

This technology stack enables AI to react and act in real time in the real world.

At the heart of physical AI are large-scale language models (LLMs) and vision language models (VLMs).

Beyond simple chatbots, they give robots the ability to understand human language, interpret visual information, and perform complex tasks.

Major learning methods include language-based imitation learning, language-assisted reinforcement learning, in-context learning (ICL), and stage-based thinking (CoT).

Physical AI moves beyond cloud-centric processing to process data immediately on-site through edge AI and on-device AI.

NVIDIA's 'Jetson Thor' and Qualcomm's 'QCS series' are low-power, high-performance platforms that maximize the response speed of robots and drones.


Sensors like LiDAR play a key role in enabling AI to precisely perceive physical spaces.

Training AI directly in the real world is expensive and risky.

To solve this, NVIDIA's 'Omniverse' and 'Isaac Simulation platforms such as 'Sim (Isaac Sim)' are utilized.

In particular, 'Cosmos' is a world foundation model that understands the laws of physics, helping virtual training results to work accurately in reality.

Physical AI goes beyond mere technological innovation; it has the potential to simultaneously improve industrial productivity and the quality of life in society.

However, achieving this requires not only technological development but also legal, ethical, and social preparations. Various challenges exist, including data privacy, changes in the labor market, and the global race for technological supremacy.

Ultimately, success in the era of physical AI will depend not on the speed of technological adoption, but on how we design the interaction between humans and intelligent machines and how we transform the operating model of organizations.

In the next series, we will discuss the market outlook for physical AI and its application in each industry.