Karine Miras, Matteo De Carlo, Sayfeddine Akhatou, A. E. Eiben

Evolving-Controllers Versus Learning-Controllers for Morphologically Evolvable Robots

In Applications of Evolutionary Computation. EvoApplications 2020. Lecture Notes in Computer Science(), vol 12104, 2020

DOI 10.1007/978-3-030-43722-0_6


We investigate an evolutionary robot system where (simulated) modular robots can reproduce and create robot children that inherit the parents’ morphologies by crossover and mutation. Within this system we compare two approaches to creating good controllers, i.e., evolution only and evolution plus learning. In the first one the controller of a robot child is inherited, so that it is produced by applying crossover and mutation to the controllers of its parents. In the second one the controller of the child is also inherited, but additionally, it is enhanced by a learning method. The experiments show that the learning approach does not only lead to different fitness levels, but also to different (bigger) robots. This constitutes a quantitative demonstration that changes in brains, i.e., controllers, can induce changes in the bodies, i.e., morphologies.