Gongjin Lan, Maarten van Hooft, Matteo De Carlo, Jakub M. Tomczak, A.E. Eiben

Learning locomotion skills in evolvable robots

In Neurocomputing, Volume 452, 2021

DOI 10.1016/j.neucom.2021.03.030


The challenge of robotic reproduction – making of new robots by recombining two existing ones – has been recently cracked and physically evolving robot systems have come within reach. Here we address the next big hurdle: producing an adequate brain for a newborn robot. In particular, we address the task of targeted locomotion which is arguably a fundamental skill in any practical implementation. We introduce a controller architecture and a generic learning method to allow a modular robot with an arbitrary shape to learn to walk towards a target and follow this target if it moves. Our approach is validated on three robots, a spider, a gecko, and their offspring, in three real-world scenarios.