From self-organization to safe robotics

By Markus Schulte-Huermann | Strategie-Team

From self-organization to safe robotics

How Knodge.eu is helping to shape the future of AI

The vision of intelligent, autonomous robots that interact seamlessly with their environment and humans is getting closer and closer. But with this development comes complex challenges, particularly the ability of these systems to deal with uncertainty and cooperate intelligently. In our recent discussions, we looked at how the robotics market is growing, the role of epistemic uncertainty and how historical concepts of self-organization and modern platforms such as Knodge.eu are shaping the future.

The changing robotics market: players and challenges

The market for humanoid robots is in full swing, with players such as Tesla ("Optimus"), Agility Robotics ("Digit") and Figure AI ("Figure 01") from the USA, as well as Chinese players such as Unitree Robotics and UBTECH Robotics. Europe is also strongly represented, with NEURA Robotics from Germany, PAL Robotics from Spain and Pollen Robotics from France Europe's robotics champions: A look at the leading companies, The market for humanoid robots 2025/2026: Everything you need to know. These robots are designed to operate in complex, dynamic environments, which raises the question of their reliability and safety.

A central challenge here is epistemic uncertainty. This is the uncertainty that results from a lack of knowledge or insufficient data and could, in principle, be reduced if the system had more information An overview of uncertainty representation and quantification in neural networks. For robots operating in the real world, it is crucial to recognize these knowledge gaps in order to make safe and trustworthy decisions International Report on AI Safety 2026.

Solutions for epistemic uncertainty

To manage epistemic uncertainty, researchers and developers rely on various strategies:

  • Uncertainty quantification in AI models: Techniques such as Bayesian Neural Networks (BNNs), which superimpose probability distributions over their weights, or Deep Ensembles, which aggregate the predictions of multiple models, help to measure uncertainty in the AI models themselves An overview of uncertainty representation and quantification in neural networks.
  • Causal world models: Robots develop internal models to understand causality and predict future states, which reduces uncertainty in planning Causal World Model for Robot Control.
  • Continuous and adaptive learning: Systems must dynamically adapt their knowledge base and learn continuously to close knowledge gaps and adapt to new circumstances Portal Knowledge Base: RAG Nested Learning.
  • Communication of uncertainty:** Robots must be able to effectively communicate their uncertainty to human operators, whether through visual indicators, voice output or adapted behavior, to ensure trust and safety in human-robot collaboration.

The roots of cooperativity and self-organization: German pioneers

The concepts of cooperativity and self-organization are not new. Some 40 years ago, German scientists such as Hermann Haken (founder of synergetics), Christoph von der Malsburg (self-organizing neural maps) and Hans Meinhardt (pattern formation in biological systems) developed fundamental theories on how complex structures and intelligent behaviour can emerge from the interaction of many simple components, without central control.

Haken's synergetics describes how "order parameters" enslave the behavior of individual parts and, conversely, are generated by them, providing a blueprint for cooperative networks. Von der Malsburg's work showed how neural networks develop functional maps through local interactions, and Meinhardt's research shed light on pattern formation in biological systems. These interdisciplinary approaches laid the foundation for our current understanding of adaptive, intelligent systems.

Knodge.eu: Bridge between theory and practice

Knodge.eu is perfectly positioned to combine these historical visions with the requirements of modern AI and robotics:

The future of robotics lies in the ability to manage uncertainty and operate in intelligent, cooperative networks. Knodge.eu provides the technological platform to realize these visions and develop the next generation of robots and AI systems that are not only powerful, but also safe, reliable and trustworthy.

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