Physical AI Why Robots Need a "Childhood" to Become Autonomous

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Not computing power alone, but above all high-quality real-world experience data becomes the decisive factor for powerful autonomous systems. Why robots, like children, need millions of hours of practical learning experience and why simulations are only part of the solution.

In generative text or image models in AI, learning processes operate entirely in the virtual space. Models are fed with large amounts of text or images, and their outputs are then analyzed to continuously improve the quality of their responses. In robotics, however, learning is a physical process. For robots to understand their environment and act independently, they must gain experience in the real world.

Physical AI is therefore not a finished product that can be implemented overnight. Similar to humans, it needs to develop gradually. Children learn in their early years through constant practice and imitation of their surroundings, mastering safe movement, balance, and purposeful interaction with their environment. Likewise, robots initially develop their skills through simulations and the imitation of human movements. By experimenting, repeating, and correcting, they expand these skills and learn to handle new situations. They must grasp, balance, fail, correct, and gain experience with each movement to then reliably perform the tasks entrusted to them.

From Data Point to World Understanding

In robotics, intelligence is not solely generated through training in simulations or the imitation of human movements. The critical difference for robust autonomy, however, lies in real-world experiences: every movement a robot makes, every grip, collision, and correction generates a data point that sharpens the robot's understanding of its environment and reality. The more diverse these experiences are, the better the system can recognize patterns and derive generalized conclusions. This enables it to apply skills even in situations that were not part of the original training environment.

Before a human can navigate safely in different environments, they spend tens of thousands of hours engaged in physical activity. Robots can process such motion data much faster than humans, but to achieve a comparable intuitive understanding of the physical world, they still require a multitude of experience, which can only be gained through millions of operational hours. Building a sufficiently large and, above all, high-quality data foundation is therefore anything but trivial. For this reason, the availability of training data is increasingly becoming the critical bottleneck for Physical AI. Algorithms remain the foundation, but the key differentiator in the future will be the best training data.

The Limits of Virtual Training Worlds

Unlike generative AI, the demand for data becomes an even greater challenge than providing additional computing power. While language models can access an almost unlimited amount of digital data, robots must first generate their training data through real-world interactions. It seems logical to produce the necessary experiences in simulations. Virtual training environments already play an important role today, as they are cost-effective and enable millions of training iterations. Yet, simulations have their limits. The real world is full of small irregularities that are difficult to model: varying surfaces, material deviations, changing lighting conditions, sensor noise, or unpredictable disturbances.

Simulations can thus convey basic behavioral patterns but cannot replicate all sensory feedback and unforeseen situations that are crucial for navigating the real world. Just as children do not learn to ride a bike by watching others, robots cannot learn to act skillfully without trying out movements under real-world conditions themselves.

Learning like in the Real World

For this reason, companies are working on giving robots a "childhood." They create structured training environments that include various tasks with different materials, diverse conditions, and lighting scenarios. Within these environments, machines can repeat tasks as often as needed and continuously learn from their experiences. The larger and more diverse these training environments become, the faster Physical AI can develop.

This principle is not new. Just as search engines were only able to deliver increasingly precise results through the analysis of billions of actual search queries, Physical AI today also improves through motion data from real interactions.

Training Data Becomes a Strategic Advantage

In the development of Physical AI, the greatest bottleneck is not the available computing power but access to sufficiently high-quality training data from real application scenarios. The future of autonomous systems will not only be determined by who develops the most complex algorithm but also by who creates the best conditions for continuous learning from real data. Companies that can generate, share, and utilize large amounts of experiential data will play a decisive role in shaping how robots develop in the future.

Ultimately, powerful Physical AI is not developed in isolation in the lab but in industrial environments, under practical production conditions, and along actual customer requirements. Real experience data is not a nice-to-have but the foundation.

*Clemens Marschner is Principal Engineer at RobCo, where he and his team work on AI-powered autonomous robotic systems for industrial applications. He is responsible for the development of physical AI systems and the underlying technical architectures for the next generation of industrial robotics. Previously, he held senior engineering roles at Microsoft and Lyft, working on large-scale machine learning systems for search engines and autonomous driving. Marschner earned his doctorate at LMU Munich and has been developing AI applications in Munich for over 15 years.

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