Humanoids: Just the Tip of the Iceberg Study: Sensor Technology, Semiconductors, and Software Are Benefiting from the Robotics Boom

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A study by Fraunhofer IPA takes a closer look at what lies behind the hype surrounding robots. Its conclusion: Humanoids are just the tip of the iceberg. Value is created in data, simulation, sensor technology, semiconductors, and integration.

Humanoid robots dance on stages, carry boxes, or work in demonstration factories. But anyone who attributes the transformation in robotics solely to human-like machines is missing the bigger picture. This is the conclusion reached by the study “AI Meets Robotics – Physical AI, Humanoid Robots, and the Future of Automation,” which the Fraunhofer IPA conducted for the Feri Cognitive Finance Institute. It examines AI-based robotics from both a technological and an economic perspective.

The authors see the real transformation taking place deeper in the value chain. Robots are expected to use AI to perceive their environment, learn from data, and solve tasks more flexibly. This requires not only powerful models, but also sensors, actuators, computing hardware, simulation, and data platforms. The study identifies precisely this interplay as the foundation for the further development of Physical AI.

Established Robotics Meets a Young Market

Market data illustrates the varying degrees of development across the segments: In 2024, more than 542,000 new industrial robots were installed worldwide, bringing the total installed base to approximately 4.66 million systems. China accounted for about 54 percent of the new installations. In the professional service robot sector, approximately 199,000 systems were sold, with more than half of these used in transportation and logistics. Medical service robots saw a 91 percent increase.

Humanoids, on the other hand, are still in their infancy. Demonstrators and pilot projects are attracting significant investment, but there is not yet an established mass market. According to the study, venture capital in this segment surged to approximately $6.1 billion in 2025. The study also puts the projected annual growth rate of more than 100 percent through 2029 into perspective by noting the small starting point and widely divergent long-term forecasts.

The authors therefore expect these robots to complement—rather than replace—existing robotics technology, at least initially. Humanoids could be used in situations where variable tasks or workstations designed for humans make traditional automation difficult. Impressive stage demonstrations, on the other hand, are not evidence of imminent mass adoption—especially since such systems are sometimes teleoperated and specifically prepared for demonstrations.

Data and Integration Are Becoming a Bottleneck

For electronics developers, a significant part of this transformation lies deeper in the value chain. The study cites software, data, and simulation environments, as well as sensor technology, semiconductors, and other enabling technologies. Added to this are actuators, robotic hands, data platforms, integrators, and service providers. As a result, a distinct supplier and integration ecosystem is emerging around Physical AI that extends far beyond manufacturers of complete humanoid systems.

The data infrastructure remains a hurdle. In addition to generalizability, security, cost-effectiveness, regulation, and acceptance, the study explicitly cites data bottlenecks. Providers and integrators must therefore build up not only hardware but also training data, simulation expertise, and process knowledge. Only the interplay of these components makes it possible to robustly integrate adaptive systems into industrial processes.

Pilot First, then Scale

For users, the study derives a clear course of action: Start with a specific problem, not a technology trend. Companies should first select narrowly defined applications—such as in intralogistics or repetitive handling tasks—test their robustness and cost-effectiveness in pilot operations, and only then scale up.

Standards, liability, and safety should also be incorporated early in the development process. Clear operational concepts and certification are particularly necessary when robots are used in close proximity to people. For Europe, the study identifies strengths in research, mechanical engineering, and industrial automation, but also shortcomings in scaling, venture capital, data infrastructure, and rapid implementation. Its guiding principle remains deliberately pragmatic: automation should not be an end in itself. What matters is where AI-based robotics can better solve a specific technical or economic problem. 

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