Nvidia Says Physical AI Reshapes Robotics Competition
As the stage of artificial intelligence expands beyond software into the real world, the standards of robot competition are also changing. As ‘physical AI,’ in which AI that remained as screen-based intelligence is implemented as actual actions of robots, emerges as a core competitive edge, Samsung
Artificial intelligence is no longer restricted to software; it is now being integrated into physical robots, fundamentally altering the landscape of robot competitions. Samsung Electronics and LG Electronics are rapidly enhancing their physical AI capabilities by leveraging large-scale data accumulated from their manufacturing sites and living spaces.
They are targeting this market by merging AI and robotics, based on extensive data. Nvidia's Vice President of Physical AI Simulation, Rev Lebaredian, emphasized that physical AI enables intelligence in machines operating in the real world. He noted that robotics and autonomous systems will become increasingly crucial in sectors like manufacturing, logistics, agriculture, and energy.
Lebaredian argued that AI's role is shifting from supporting human tasks to performing work directly. This requires large-scale computing infrastructure to translate AI judgments into robots' physical actions. Companies like Samsung and LG are converting their business foundations into physical AI competitiveness. Samsung plans to transform its global production bases into 'AI-driven factories' by 2030, integrating digital twin-based simulations into all processes.
It will apply AI agents to quality, production, and logistics, and deploy manufacturing robots like logistics bots and assembly bots. Conversely, LG is using manufacturing site data for robot learning and expanding physical AI into living spaces, employing a vision-language model and action model in its home robot 'LG CLOiD'. They are constructing a 'data flywheel' with Nvidia, utilizing manufacturing and logistics data for robot learning.
The company believes that industry-specific knowledge is vital for connecting physical AI to solving real-world problems, stating that those who understand an industry's challenges best are often in the best position to innovate within that industry.
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