Directed locomotion for modular robots with evolvable morphologies

Gongjin Lan*, Milan Jelisavcic, Diederik M. Roijers, Evert Haasdijk, A. E. Eiben

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review

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Morphologically evolving robot systems need to include a learning period right after ‘birth’ 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.

Original languageEnglish
Title of host publicationParallel Problem Solving from Nature – PPSN XV
Subtitle of host publication15th International Conference, Coimbra, Portugal, September 8–12, 2018, Proceedings, Part I
EditorsCarlos M. Fonseca, Nuno Lourenco, Penousal Machado, Luis Paquete, Darrell Whitley, Anne Auger
Number of pages12
VolumePart 1
ISBN (Electronic)9783319992532
ISBN (Print)9783319992525
Publication statusPublished - 2018
Event15th International Conference on Parallel Problem Solving from Nature, PPSN 2018 - Coimbra, Portugal
Duration: 8 Sep 201812 Sep 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11101 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th International Conference on Parallel Problem Solving from Nature, PPSN 2018


  • Evolutionary robotics
  • Evolvable morphologies
  • Modular robots
  • Gait learning
  • Directed locomotion


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