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.
@article{arxiv.2010.09531,
title = {Learning Locomotion Skills in Evolvable Robots},
author = {Gongjin Lan and Maarten van Hooft and Matteo De Carlo and Jakub M. Tomczak and A. E. Eiben},
journal= {arXiv preprint arXiv:2010.09531},
year = {2020}
}