Physical AI: Five questions from the factory floor

After our Physical AI webinar with SICK, NVIDIA, and Inbolt, one thing stood out: The volume and quality of questions coming from manufacturers trying to understand what Physical AI really means in production.

To continue the conversation, five of those questions were selected and put directly to Vice President of AI Robotics Products at Universal Robots Anders Billesø Beck. Hear his perspective in the video below, based on current questions and ongoing work with customers and partners.

Anders begins with safety, still one of the biggest hurdles for any new technology. As he puts it, “safety is going to be one of the key stumbling blocks for a lot of technologies to get into the market.” At the same time, he stresses that the fundamentals are familiar. The safety considerations around Physical AI are largely the same as for any other robot application.

Every deployment, AI-driven or not, must start with a proper risk assessment. Universal Robots’ systems include a safety envelope that runs independently of the application itself. This allows manufacturers to define maximum tolerable speeds, configure virtual safety fencing, and constrain behavior at the safety-system level. With those limits in place, “the robot can do all the variation that an AI application will have inside the envelope of safety."

The second question turns to GMP-regulated manufacturing, particularly pharmaceuticals. Anders is direct about the challenge. “There’s no doubt there’s a high degree of need for validation, for certification, and really understanding that there’s a repeatable outcome for your process all the time.”

There is progress, he said, but no shortcuts. There is no readymade cookbook for validating Physical AI in regulated environments. Some manufacturers, however, are now “at the cusp of having done statistical validation, statistical analysis on Physical AI models.” That work is helping teams prove consistency and define operational envelopes, but it is not straightforward.

Performance and predictability come next. Can Physical AI deliver deterministic behavior, including worst-case latency? In many cases, yes, especially when applications are tightly scoped.

“The more we limit the impact of an application where we apply Physical AI models, the better the performance and validation is,” Anders explains.

Smaller, task-specific models, such as those for computer vision or tactile control, are already showing strong results. Fully end-to-end AI systems remain cutting-edge and harder to validate over time. Architecture matters. Many AI models run at “somewhere between 10 and 30 hertz,” while industrial robot controllers operate at much higher, deterministic rates. By integrating AI outputs into the robot’s real-time control layers, slower reasoning can be combined with fast, reactive motion, reaching “all the way down to 500 hertz.”

The fourth question looks at existing installations. Physical AI can be used with UR eSeries robots running PolyScope 5, particularly when AI workloads are handled externally and real-time control remains on the robot. At the same time, PolyScope X** and the **UR Series are designed specifically with Physical AI in mind, offering a more integrated foundation going forward.

Finally, Anders addresses where Physical AI still struggles. While areas like computer vision and inspection are progressing quickly, challenges remain “where the physics are challenging.” Tasks requiring extreme accuracy, repeatability, or high levels of variation continue to be difficult and remain active areas of development.

Taken together, these five questions offer a grounded snapshot of Physical AI today. They show what is working, what is possible, and where the hardest problems still lie.