Guiding the Exploration of the Solution Space in Walking Robots Through Growth-Based Morphological Development

Martin Naya, Andres Faina, Richard Duro

Publikation: Konference artikel i Proceeding eller bog/rapport kapitelKonferencebidrag i proceedingsForskningpeer review


In human beings, the joint development of the body and cognitive system has been shown to facilitate the acquisition of new skills and abilities. In the literature, these natural principles have been applied to robotics with mixed results and different authors have suggested several hypotheses to explain them. One of the most popular hypotheses states that morphological development improves learning by increasing exploration of the solution space, avoiding stagnation in local optima. In this article, we are going to study the influence of growth-based morphological development and its nuances as a tool to improve the exploration of the solution space. We will perform a series of experiments over two different robot morphologies which learn to walk. Furthermore, we will compare these results to another optimization strategy that has been shown to be useful to favor exploration in learning algorithms: the application of noise during learning. Finally, to check if the increased exploration hypothesis holds, we visualize the genotypic space during learning considering the different optimization strategies by using the Search Trajectory Network representation. The results indicate that noise and growth increase exploration, but only growth guides the search towards good solutions.
TitelGECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference
ForlagAssociation for Computing Machinery
StatusUdgivet - 2023
BegivenhedGECCO '23: Genetic and Evolutionary Computation Conference - Lisbon , Portugal
Varighed: 15 jul. 202319 jul. 2023


KonferenceGECCO '23: Genetic and Evolutionary Computation Conference


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