Governance around Physical AI is becoming harder as autonomous AI systems move into robots, sensors, and industrial equipment. The issue is not only whether AI agents can complete tasks. It is how their actions are tested, monitored, and stopped when they interact with real-world systems.

Industrial robotics already provides a large base for that discussion. The International Federation of Robotics said 542,000 industrial robots were installed worldwide in 2024, more than double the annual level recorded a decade earlier. It expects installations to reach 575,000 units in 2025 and pass 700,000 units by 2028.

Market researchers are also applying the Physical AI label to a wider group of systems, including robotics, edge computing, and autonomous machines. Grand View Research estimated the global Physical AI market at US$81.64 billion in 2025 and projected it to reach US$960.38 billion by 2033, though the category depends on how vendors define intelligence in physical systems.

From model output to physical action

The governance challenge is different from software-only automation because physical systems can operate around workplaces, infrastructure, and human users. They can also be connected to equipment that requires clear safety limits. A model output can become a robot movement or a machine instruction. It can also become a decision based on sensor data. That makes safety limits and escalation paths part of system design.

Google DeepMind’s robotics work is one recent example of how AI models are being adapted for this environment. The company introduced Gemini Robotics and Gemini Robotics-ER in March 2025, describing them as models built on Gemini 2.0 for robotics and embodied AI. Gemini Robotics is a vision-language-action model designed to control robots directly, while Gemini Robotics-ER focuses on embodied reasoning, including spatial understanding and task planning.

A robot using this type of model may need to identify an object, understand an instruction, and plan a sequence of movements. It also needs to assess whether the task has been completed correctly. That creates a control problem that includes both model behaviour and the mechanical limits of the system.

Google DeepMind said useful robots need generality, interactivity, and dexterity. Generality covers unfamiliar objects and environments. Interactivity relates to human input and changing conditions. Dexterity refers to physical tasks that require precise movement.

In its launch materials, Google DeepMind said Gemini Robotics could follow natural-language instructions and perform multi-step manipulation tasks. Examples included folding paper, packing items into a bag, and handling objects not seen during training.

Read More: Physical AI raises governance questions for autonomous systems