@inbook{e227abd78181489d940cb53c55b9afb2,
title = "Directed locomotion for modular robots with evolvable morphologies",
abstract = "Morphologically evolving robot systems need to include a learning period right after {\textquoteleft}birth{\textquoteright} to acquire a controller that fits the newly created body. In this paper, we investigate learning one skill in particular: walking in a given direction. To this end, we apply the HyperNEAT algorithm guided by a fitness function that balances the distance travelled in a direction and the deviation between the desired and the actually travelled directions. We validate this method on a variety of modular robots with different shapes and sizes and observe that the best controllers produce trajectories that accurately follow the correct direction and reach a considerable distance in the given test interval.",
keywords = "Evolutionary robotics, Evolvable morphologies, Modular robots, Gait learning, Directed locomotion",
author = "Gongjin Lan and Milan Jelisavcic and Roijers, \{Diederik M.\} and Evert Haasdijk and Eiben, \{A. E.\}",
year = "2018",
doi = "10.1007/978-3-319-99253-2\_38",
language = "English",
isbn = "9783319992525",
volume = "1",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer/Verlag",
pages = "476--487",
editor = "Fonseca, \{Carlos M.\} and Nuno Lourenco and Penousal Machado and Luis Paquete and Darrell Whitley and Anne Auger",
booktitle = "Parallel Problem Solving from Nature – PPSN XV",
note = "15th International Conference on Parallel Problem Solving from Nature, PPSN 2018 ; Conference date: 08-09-2018 Through 12-09-2018",
}