Sommersemester 2024

Evolutionary Robotics


Evolutionary robotics belongs to the field of artificial intelligence, in particular machine learning. Key idea is to use optimization methods to synthesize controllers for mobile robots.

"Evolutionary robotics is a new technique for automatic creation of autonomous robots. It is inspired by the Darwinian principle of selective reproduction of the fittest. It is a new approach which looks at robots as autonomous artificial organisms that develop their own skills in close interaction with the environment without human intervention. Heavily drawing from natural sciences like biology and ethology, evolutionary robotics makes use of tools like neural networks, genetic algorithms, dynamic systems, and biomorphic engineering." (Nolfi and Floreano, 'Evolutionary Robotics')

"An initial population of different artificial chromosomes, each encoding the control system (and sometimes the morphology) of a robot, are randomly created and put in the environment. Each robot (physical or simulated) is then let free to act (move, look around, manipulate) according to a genetically specified controller while its performance on various tasks is automatically evaluated. The fittest robots are allowed to reproduce (sexually or asexually) by generating copies of their genotypes with the addition of changes introduced by some genetic operators (e.g., mutations, crossover, duplication). This process is repeated for a number of generations until an individual is born which satisfies the performance criterion (fitness function) set by the experimenter." (Nolfi and Floreano, 'Evolutionary Robotics')

In difference to the application of evolutionary algorithms in optimization, challenges in evolutionary robotics are autonomy, time efficiency, and the complexity of fitness space and search space. The course covers the biological fundamentals, an overview of evolutionary algorithms, artificial neural networks, how to evolve robots, co-evolution, and a number of sophisticated state-of-the-art techniques to improve the performance of the evolutionary robotics approach.


  • Nolfi, S. and Floreano, D. Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines, MIT Press, 2001
  • Bongard, J. C. (2013). Evolutionary robotics. Communications of the ACM, 56(8), 74-83.
  • Eiben, A. E., & Smith, J. (2015). From evolutionary computation to the evolution of things. Nature, 521(7553), 476-482.
  • Doncieux, S., Bredeche, N., Mouret, J.-B., and Eiben, A. E. (2015). Evolutionary robotics: what, why, and where to. frontiers in Robotics and AI,