Abstract
A key challenge for evolving complex physical ob-jects is to design a representation, that is, to devise suitable genotypes and a good mapping from genotypes to phenotypes (the objects to be evolved). This paper outlines a new approach to address this challenge for evolving robot morphologies and presents a proof-of-concept study to assess its feasibility. The key idea is to design genotype-phenotype mappings using variational autoencoders. This idea is implemented and tested for the evolution of modular robots for a locomotion task. The experiments show the practicability of this idea where the representation is not hand-designed, but algorithmically generated. This indicates a great future potential for the evolution of complex objects where there are no straightforward representations to use.
| Original language | English |
|---|---|
| Title of host publication | 2023 IEEE Symposium Series on Computational Intelligence (SSCI) |
| Subtitle of host publication | [Proceedings] |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1072-1077 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781665430654 |
| DOIs | |
| Publication status | Published - 2024 |
| Event | 2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023 - Mexico City, Mexico Duration: 5 Dec 2023 → 8 Dec 2023 |
Conference
| Conference | 2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023 |
|---|---|
| Country/Territory | Mexico |
| City | Mexico City |
| Period | 5/12/23 → 8/12/23 |
Bibliographical note
Publisher Copyright:© 2023 IEEE.
Keywords
- evolutionary robotics
- genotype-phenotype mapping
- morphological evolution
- representation