Physical AI

Definition and Conceptual Framework

Physical AI refers to artificial intelligence systems that interact directly with the physical world through sensors, actuators, and embodied platforms such as robots, autonomous vehicles, drones, and industrial machines. Unlike digital AI, which operates entirely within software environments processing text, images, or data, physical AI must perceive three-dimensional environments in real time, reason about physical properties like mass, friction, and spatial relationships, plan sequences of physical actions, and execute those actions through mechanical systems with imperfect control. Physical AI represents the next frontier beyond the large language models and generative AI systems that dominated the 2023-2025 period.

The term gained prominence in NVIDIA CEO Jensen Huang's framing of the technology landscape, where he described physical AI as the next wave of AI development following the era of language and vision models. Korea's adoption of physical AI as one of the eight key sectors in the K-Moonshot programme reflects the government's assessment that the transition from digital-only AI to physically embodied AI represents a defining competitive frontier for the next decade.

Core Technical Components

Physical AI systems integrate multiple technical disciplines that must function together in real time. Perception encompasses the sensory systems that allow machines to understand their environment: computer vision using cameras, LiDAR for three-dimensional spatial mapping, force-torque sensors for contact detection, inertial measurement units for orientation, and increasingly sophisticated tactile sensors that provide the sense of touch essential for manipulation tasks. Modern physical AI perception stacks process data from all these modalities simultaneously, creating a rich, multimodal representation of the environment.

World models form the cognitive core of physical AI systems. These are learned representations of how the physical world behaves: that unsupported objects fall, that pushing a door requires applying force at specific locations, that liquids flow and deform, that different materials have different friction coefficients. Foundation models trained on vast datasets of physical interactions, including video, simulation data, and robotic experience, are creating increasingly capable world models that enable robots to generalise from limited experience to novel situations.

Planning and control translate understanding into action. Physical AI must generate sequences of movements that achieve desired goals while respecting physical constraints, avoiding obstacles, maintaining balance, and adapting to unexpected perturbations. Reinforcement learning, model-predictive control, and increasingly hybrid approaches that combine learned policies with physics-based reasoning are advancing the state of the art in robot control.

Korea's Physical AI Strategy

The K-Moonshot programme addresses physical AI through two complementary missions. Mission 6 (Humanoid Robots) focuses on the hardware platforms: developing Korean-built humanoid robots capable of operating in factories, service environments, and homes. Mission 7 (General-Purpose Physical AI Models and Computing Platforms) addresses the software intelligence: creating foundation models and computing infrastructure that enable robots and machines to understand and interact with the physical world autonomously.

This dual-mission structure recognises that physical AI requires excellence in both hardware and software, and that neither alone is sufficient. A humanoid robot without sophisticated AI is merely a mechanical puppet; a brilliant AI model without a capable physical platform has no means to act in the real world. Korea's strategy explicitly targets the integration of both dimensions, leveraging the country's manufacturing prowess for hardware and its growing AI research capability for software.

Korean Corporate Ecosystem

Korea's physical AI ecosystem spans the full value chain from components to integrated systems. Hyundai Motor Group, through Boston Dynamics and its internal robotics division, develops advanced mobile platforms with world-leading locomotion capabilities. Samsung Electronics, through its partnership with Rainbow Robotics, is building humanoid robots powered by its semiconductor technology and AI research. LG AI Research develops the EXAONE foundation model, which serves as the cognitive backbone for LG's KAPEX humanoid robot platform, demonstrating the hardware-software integration that defines physical AI.

Naver has deployed autonomous robots in its Sejong office complex, operating what may be the world's most advanced building-scale robot deployment with dozens of robots navigating shared spaces alongside human workers. Naver's HyperCLOVA X model and its robotics platform demonstrate how a technology company can extend language AI into physical world applications. Doosan Robotics brings precision collaborative robot expertise, while Korean startups including Bear Robotics (autonomous service robots) and Neubility (delivery robots) extend the ecosystem into specialised applications.

Foundation Models for Physical AI

The development of general-purpose physical AI models represents one of the most active research frontiers globally. These models aim to provide robots with generalised understanding of the physical world, analogous to how large language models provide generalised understanding of human language. Key approaches include vision-language-action (VLA) models that accept visual input and language instructions and output robot actions, world simulators that predict the physical consequences of actions before execution, and diffusion-based policy models that generate complex multi-step action sequences.

Korea's sovereign AI programme, which targets 260,000 NVIDIA GPUs for national AI computing infrastructure by 2030, provides the computational foundation for training physical AI models at scale. The Ministry of Science and ICT has funded five foundation model consortia, several of which are developing multimodal capabilities relevant to physical AI. NVIDIA's direct engagement with Korean partners, including GPU supply agreements and joint research programmes, accelerates access to the specialised hardware and software tools (such as NVIDIA's Isaac platform for robot simulation) essential for physical AI development.

Applications and Market Potential

The application domains for physical AI span manufacturing, logistics, agriculture, healthcare, construction, and domestic service. In manufacturing, physical AI enables robots to handle unstructured tasks such as bin picking, flexible assembly, and quality inspection that traditional industrial automation cannot address. In logistics, autonomous mobile robots (AMRs) equipped with physical AI navigate dynamic warehouse environments without fixed infrastructure. In healthcare, physical AI enables surgical assistance, rehabilitation robotics, and elder care.

Market projections for physical AI vary widely but consistently indicate massive scale. The combined market for robots, autonomous vehicles, drones, and industrial automation systems enhanced by AI is projected to exceed $500 billion by 2035. Korea's demographic trajectory, with the world's lowest fertility rate and a rapidly ageing population, creates domestic urgency for physical AI solutions that can supplement a shrinking workforce, giving the country both the motivation and the market conditions to become an early large-scale adopter.

Relationship to Korea's Industrial Base

Physical AI plays to Korea's historical strengths in ways that purely digital AI does not. Korea's economy has been built on manufacturing excellence in automobiles, ships, electronics, batteries, and semiconductors, all industries that involve mastery of physical production processes. Physical AI extends these capabilities rather than disrupting them: a physical AI-enhanced factory builds on existing Korean manufacturing infrastructure, a physical AI-powered autonomous ship leverages Korean shipbuilding expertise, and humanoid robots manufactured by Korean chaebols utilise established supply chains and precision engineering capabilities.

This strategic alignment between physical AI and Korea's industrial base is a key reason the K-Moonshot programme prioritises the sector. While Korea may face challenges competing with the United States and China in foundational AI model research, the integration of AI with physical systems represents a competitive domain where Korea's manufacturing heritage provides genuine structural advantages.