Why in news?
Industrial uses of artificial intelligence have drawn fresh attention following ADNOC’s partnership with OpenAI and G42, announced in Abu Dhabi on 2 November 2026. ADNOC, the Abu Dhabi National Oil Company, described a plan to connect AI agents across its operations. Within the wider field of industrial AI, physical AI concerns systems that perceive their surroundings and act through machines. Examples include warehouse robots, robotic arms and autonomous vehicles. Understanding these systems requires following the connection between sensing, decisions and physical movement.
From observations to action
A physical AI system combines computing with a machine’s sensing and control equipment. Sensors collect observations: a camera supplies images, for example, while a distance sensor helps locate nearby objects. Software uses these observations to estimate the machine’s situation and identify features relevant to its task.
The system then chooses an action and sends commands to the machine. Actuators turn those commands into physical movement. An electric motor can turn a wheel or move a robot joint. The machine’s actual movement produces new observations, allowing the controller to adjust its next action.
A warehouse example
Consider a mobile robot asked to carry a box to a storage area. It needs information about its own position, the destination and obstacles along the way. Its planning system selects a route. Its controller translates the intended movement into commands for the wheels.
If an obstacle appears, fresh sensor readings change the situation the robot must handle. The system may slow, stop or choose another route, depending on its design and operating limits. This repeated observation and adjustment is a feedback loop. It connects the outcome of an action to the next decision.
Learning and testing in simulation
Developers use real-world observations and simulated environments to train and evaluate physical AI. A simulation represents how a machine and its surroundings behave. Teams can vary object positions, lighting or movement to examine how a system responds across many situations.
A digital twin represents a particular physical machine, space or system. A warehouse twin, for example, can help teams examine layouts and robot movements before changing the working facility. Simulated and real tests complement each other because actual friction, sensor errors and machine behaviour can differ from the model.
Why reliable control matters
Physical tasks involve spatial relationships, timing and contact with objects. A robot placing a part needs to account for its location and movement while staying within the machine’s operating limits. The sensing, planning and control stages must work together quickly enough for the task.
Developers therefore assess behaviour in the intended environment, including unusual situations and interactions with people or other machines. Monitoring and defined recovery actions help manage deviations during operation. These requirements make engineering, evaluation and continuing maintenance central to deployment.
Conclusion
Physical AI connects observations, decisions and machine action in a continuing feedback loop. Its practical value depends on how reliably that loop performs the intended task in the conditions where the machine will work.
Sources
- ADNOC, 2 November 2026: partnership with OpenAI and G42 at ADIPEC
- The Indian Express, 8 November 2026: physical AI and industrial applications
- NVIDIA: physical AI, simulation, digital twins and deployment
- Zhang and colleagues, Physical AI: Concepts, Techniques, and Challenges, preprint submitted 19 October 2026; sections 1.3 and 7.5