The evolution of robot bodies and brains allows researchers to investigate which building blocks are interesting for evolving Artificial Life, and how controllers and morphologies can be shaped together for automated robot design. This tutorial aims to introduce evolution of robot body and control, and some of the key challenges one faces when doing experiments in Evolutionary Robotics. These include finding good ways to represent robots (genotypic encodings), challenges related to co-optimizing morphology and control, how environments shape body and control, and selecting the right physical substrate for evolving robots.
After introducing these challenges and showing relevant examples from our own and other labs’ research, we will present a short demo of how to run Evolutionary Robotics experiments in practice, with the Unity ML-Agents framework.
The tutorial requires no prior knowledge of robotics, and is appropriate for beginners. A certain familiarity with Evolutionary Algorithms can be helpful, but we aim to introduce the most central topics in a way everyone can understand. The focus of this tutorial is complementary to the tutorial “SimER: Simulation in Evolutionary Robotics”: Whereas we go into more detail about the opportunities and challenges related to evolving robots, the SimER-tutorial adds much more depth on the different tools for simulation and visualization that are relevant for the field.