Physical AI moves beyond traditional robotics

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Physical AI is finding new applications beyond conventional robotics as enterprises test and use AI-enabled robotic systems in data centers, manufacturing plants and warehouses.

These systems combine robotics with AI capabilities, such as computer vision, decision-making and world models -- AI models designed to represent physical environments and predict how they might change in response to different actions.

Meta, for example, is testing robots at several of its data centers for tasks traditionally performed by technicians, such as swapping network cables and reseating hardware components. At the social media giant's Altoona, Iowa, campus, dual-armed robots have been practicing network-cable swaps. At its newer Prometheus facility in New Albany, Ohio, Meta is testing robots to reseat hardware components, while also exploring whether a robotic arm can power-cycle servers.

Similar applications are emerging in other industries. Automaker Nissan is deploying AI-powered autonomous mobile robots to transport components in its Smyrna, Tenn., factory and logistics operators, such as DHL Supply Chain, are using robots to guide workers through warehouse picking.

While applications of physical AI vary widely, they share a practical focus: using robots for work that's repetitive, physically demanding or hard to scale with human labor.

Physical AI isn't the same as traditional robotics

Robots have worked at enterprises for decades. Traditional industrial robots typically execute predefined instructions in controlled environments. Physical AI adds perception and decision-making capabilities, enabling robots to respond to conditions they weren't explicitly programmed to handle.

World models can support this kind of adaptability by helping robots predict how the physical environment might change in response to different actions before they act.

Where enterprises are actually using physical AI

Data centers

Manufacturing

Warehouses and logistics

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