Learning Locomotion Skills in Evolvable Robots [article]

Gongjin Lan, Maarten van Hooft, Matteo De Carlo, Jakub M. Tomczak, A.E. Eiben
<span title="2020-10-19">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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
more &raquo; ... 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.
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