Robot Intelligence in the Synthesis of Life

How did life emerge? Could it exist elsewhere? Could we even synthesize life – a system that is self-sustaining, self-replicating and evolving? The TUM Innovation Network for Robot Intelligence in the Synthesis of Life (RISE) aims to develop a radically new approach to these centuries-old questions, combining machine learning and robotics with chemical and biophysical experiments.

Robots will not only take tedious tasks out of the scientists’ hands, but actually become part of the experiments. By allowing the robots to observe data in real-time, let them analyze experiments, and, via artificial intelligence, change the course of the experiments, the scientists anticipate that a self-learning experiment evolves towards a system that displays the essential hallmarks of life. It is a development with the potential to revolutionize research and development in both industry and academia.

Our Team

Doctoral theses

  • Modular Automation for Cell Culture Tasks regarding the cultvation of iPS and other cells within incubators (Lucas Artmann)
  • Planning and Learning of Robotic Knowledge Discovery Processes (Moritz Eckhoff)
  • Exploring self-replication in model systems based on DNA origami (Markus Eder)
  • Autonomous Machine Learning through Artificial Intelligence in chemical and robotic Systems (Lukas Eylert)
  • Synthetic DNA-protein hybrid molecular devices and machines (Christopher Frank)
  • Reverse Engineering transcriptional regulation modules and inferring single molecule dynamics within DNA structure (Frauke Huth)
  • Design and evolution of small metalloproteins for catalysis and implementation in reaction networks (Robert Klassen)
  • Self-replication, self-sustenance and self-learning in chemical and robotic systems (Anton Maier)
  • Automation and self-learning experiments towards the synthesis of life (Monika Wenisch)


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