Building a Physical AI Ecosystem for AI Transformation in Manufacturing

Physical AI technologies, humanoid robots among them, are expected to reach commercial use first in manufacturing, where interaction with people is comparatively limited. Raising the competitiveness of manufacturing through autonomous production, in which human intervention is minimized, will require a manufacturing artificial intelligence (AI) transformation (AX) built on physical AI and capable of handling the wide range of unstructured tasks a factory presents. Viewed from that standpoint, the physical AI ecosystem has six core elements: form factor development and design, manufacturing-specific AI behavior learning, actuators, robot hands, AI foundation models, and on-device AI semiconductors. South Korea holds a comparative advantage in manufacturing-specific AI behavior learning, and this study proposes a K-Physical AI Ecosystem Expansion Strategy that uses that advantage as a bridgehead for widening Korea’s presence across the ecosystem. Reaching the goal of joining the global top three in AI robots, set out in the Three Megaprojects for a Great Leap Forward for South Korea, depends on accumulating manufacturing-specific behavior learning and data built on a Korean physical AI robot form factor of the country’s own. What is also needed is a manufacturing AX roadmap by industry and process, one that widens the scope of application in stages, moving from AI robots for light work to general-purpose and then heavy-duty applications as physical AI technology matures and conditions on the factory floor allow. For the physical AI transition in Korea’s flagship manufacturing industries, policy should be pursued at the level of the industry and the industrial complex, matched to the form of AI robot that each industry and process requires.