Interview
Sim-to-Real - The next Challenge for Robotics and Physical AI
The era where Artificial Intelligence only existed on a screen is officially over. With the rise of Physical AI, intelligence is moving directly into machines and robots. But while training systems in digital twins and simulations is already incredibly fast and efficient, the true test begins the moment they step into the real world.
In this interview, we dive into the "Sim-to-Real gap," explore why the messy reality is so challenging for robots, and discuss how innovation ecosystems like BadenCampus help bring virtual breakthroughs reliably onto the physical shopfloor.
Viola Erb: In our previous interview (Robotics and AI), we discussed digital twins and training robots in virtual environments. Why is simulation becoming so important?
Uziel Zontag: Think of it like learning to drive. Most people start in a simulator before entering real traffic. Robots are no different. Training them directly in factories or hospitals can be expensive, slow, and risky. In simulation, robots can practice millions of times without causing damage or interrupting operations. It is faster, safer, and much more cost-effective.
Viola Erb: If simulation is so powerful, why can’t we simply train robots virtually and deploy them immediately?
Uziel Zontag: Because reality is messy. Simulations are controlled environments where everything behaves as expected. The real world is full of surprises. Lighting changes, objects are misplaced, equipment ages, and people behave unpredictably. A robot that performs perfectly in simulation may struggle when faced with these real-world conditions. This difference is known as the Sim-to-Real gap.
Viola Erb: Can you give us a simple example?
Uziel Zontag: Imagine teaching a robot to pick up coffee cups. In simulation, every cup may look identical and always be in the same position. Then you place the robot in a real cafeteria. Suddenly there are different cup sizes, reflections from windows, moving people, and unexpected obstacles. The task becomes much more challenging.
Viola Erb: How are companies addressing this challenge?
Uziel Zontag: Increasingly, companies are adopting a Sim-to-Real-to-Real approach. First, robots learn in simulation. Then they are tested in controlled environments such as pilot factories or innovation labs. Finally, they are deployed in real operational settings where they continue to learn and improve.
Viola Erb: What role can innovation ecosystems like BadenCampus play?
Uziel Zontag: They can act as the bridge between technology development and industrial deployment. BadenCampus can provide an environment where startups, researchers, and industrial partners collaborate, test solutions, validate performance, and build confidence before scaling into production environments.
Viola Erb: Why is this topic becoming especially relevant now?
Uziel Zontag: Because we are entering the age of Physical AI. AI is moving beyond software and into machines that interact with the physical world. The ability to successfully transfer intelligence from simulation into reality will become one of the key factors determining which organizations lead the next wave of innovation.
Viola Erb: Looking ahead, what do you think success will look like?
Uziel Zontag: Success will not be measured by how impressive a robot looks in a laboratory. It will be measured by how reliably it performs in the real world. Closing the Sim-to-Real gap is becoming both a technical and a business challenge.
Viola Erb: One final thought for our audience?
Uziel Zontag: The future challenge is no longer teaching robots in simulation. The real challenge is helping them succeed in the messy, unpredictable world we live in. The ecosystems that can bridge that gap will help shape the future of Physical AI.
Conclusion
The interview makes one thing clear: as we enter the era of Physical AI, the real challenge is bridging the Sim-to-Real gap. A robot’s success is not measured by flawless performance in a perfect simulation, but by its reliability in the messy, unpredictable environment of a real factory.
To master this transition, companies must embrace a phased approach—moving from simulation to controlled test labs, and finally to production. Innovation ecosystems like smartXautomation and the BadenCampus provide the vital bridge needed to connect startups, research, and industry, turning tech potential into scalable, real-world success.