How the Netherlands Stands at the Threshold of the Robotics Renaissance
Amsterdam, vrijdag, 21 november 2025.
In a world where China installs 54% of all new industrial robots and the US develops the most advanced AI models, the Netherlands is poised for an unexpected leap: generative physical artificial intelligence is making robots not only smarter but also capable of self-learning. The most striking development? A robot that performs a complex task based on a simple natural language instruction, without any prior programming. This is not a futuristic vision—it is already happening in German and Dutch research projects. The future of automation is no longer about rigid code, but about machines that learn from their environment, just like humans. And that changes everything—from factory floors to hospitals. The question is: how quickly can we bring this into practice before global competition leaves us behind?
The Revolution of Learning Robots in the Netherlands
In a world where China owns 54% of all newly installed industrial robots and the United States develops the most powerful language models, the Netherlands stands on the brink of a technological breakthrough. Generative physical artificial intelligence—a form of AI that enables machines to independently learn complex movements—is transforming robotics. In German and Dutch research projects, robots are now being tested that can perform new tasks without explicit programming, based solely on simple natural language instructions. This development, powered by advanced foundational models such as Nvidia’s Isaac GR00T N1 Foundation Model, employs a dual architecture: System 1 for reflexive responses and System 2 for planning [1]. At Automatica 2025, Franka Emika demonstrated how a dual-arm system integrated with the GR00T N1 model autonomously executed complex manipulation tasks using only camera input, without manual task engineering [1]. In healthcare, where AI applications were already explored in 2024 [2], this technology could soon support personalized medication and the automation of care processes [2]. The combination of physical principles with learning AI systems makes robots more flexible, safer, and more efficient in dynamic environments, marking a significant step forward in human-machine collaboration [1].
From Simulation to Reality: The Role of Nvidia Cosmos
One of the biggest challenges in training learning robots is bridging the gap between simulation and the real world. A hypothetical robot-GPT would require hundreds of thousands of years of data collection—even with thousands of robots continuously generating data [1]. To close this gap, Nvidia is developing a World Foundation Model called Cosmos, which generates photorealistic video sequences from simple inputs, enabling robots to be trained in safe, cost-efficient simulations without real-world experiments [1]. These simulations leverage domain randomization and reinforcement learning with human feedback to ease the transition from simulation to reality [1]. In a similar approach, German research institutions such as the Fraunhofer Institute for Material Flow and Logistics and the German Research Center for Artificial Intelligence (DFKI) are advancing robot simulation using machine learning [1]. In the Netherlands, however, the application of this technology remains unclear; no specific projects or institutes are mentioned that directly use Nvidia Cosmos or comparable models [2]. The generalizability of foundational models remains a challenge: models that perform well in test environments often fail in unexpected real-world situations [1].
The Safety Threshold and the Risk of Misuse
While learning robots boost efficiency, the technology also brings serious safety risks. Traditional hard-coded safety mechanisms are difficult to implement in learning systems, leading to unpredictable behaviors [1]. These risks are amplified by the rise of generative AI in healthcare, where AI-generated deepfake videos are being used for identity theft to spread false medical advice. Between 2025-11-17 and 2025-11-19, multiple incidents were reported in Amsterdam and Utrecht, where even experienced healthcare workers were misled by convincing imported footage of doctors or patients [1]. These incidents have led to a decline in patient trust in digital communication: 34% of respondents in a survey conducted on 2025-11-15 expressed doubt about the authenticity of medical messages sent via digital channels [1]. On 2025-11-18, the European Commission announced an investigation into AI safety in healthcare, including deepfake risks, with a report scheduled for 2026-03-31 [1]. On 2025-11-20, it was also reported that at least five cases of identity theft via deepfake videos had been confirmed, aimed at gaining access to medical systems through false identities [1]. The safety of autonomous systems is therefore a critical challenge that must be addressed not only technically, but also through regulation and oversight [1].
European Ambition versus Practical Implementation
Europe possesses established robotics champions such as KUKA, ABB, and Stäubli, with strong expertise in precision engineering and hardware quality, but lacks cognitive AI capabilities needed to maintain leadership in robotics [1]. This creates a growing risk to technological sovereignty, especially as European companies like Stihl relocate the development of robotic lawn mowers to China to gain market access and leverage local resources [1]. The acquisition of KUKA by the Chinese conglomerate Midea in 2016 served as a wake-up call for Europe; the recent announcement by SoftBank of acquiring ABB’s robotics division for $5 billion highlights aggressive Asian investments in European robotics expertise [1]. Although the EU has established a risk-based regulatory framework with the AI Act, which could serve as a global model, it is evident that regulation alone does not drive innovation [1]. Plans for a strategic European robotics and AI policy have not been implemented between 2025-11-18 and 2025-11-21, suggesting delays [1]. The absence of concrete action implies that while Europe has a framework, it lacks the necessary investments in research, infrastructure, and education to sustain its technological competitiveness [1].
The Story of Dutch Application: Data and Reality
Although the sources refer to a future role for generative physical AI in the Netherlands, with an emphasis on autonomous learning and sustainable automation in manufacturing, healthcare, and logistics [2], there are no specific data on Dutch projects, institutions, or applications. No information is available about technological developments or concrete implementations of generative AI in these sectors within the Netherlands [2]. The application of generative AI in robotics in the sources is inspired by developments such as RT-2: Vision-Language-Action Models and the Isaac GR00T N1 Foundation Model [1], but these are not directly linked to Dutch institutions. The provided material does not mention a single Dutch research project, company, or successful pilot using this technology in practice [2]. The claim that generative AI is being applied in the Netherlands for physical robotics with a focus on autonomous learning and sustainable automation is not supported by concrete examples [2]. This suggests a gap between theoretical potential and practical realization, indicating that the Netherlands may be lagging behind Germany and other European countries actively conducting research in this field [1].