A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit <a rel="external noopener" href="https://arxiv.org/pdf/2010.09531v1.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
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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<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2010.09531v1">arXiv:2010.09531v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/m5mmiy2ekzbwnerfugu7ntrj6q">fatcat:m5mmiy2ekzbwnerfugu7ntrj6q</a> </span>
more »... 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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