Exploring Robot Morphology Spaces through Breadth-First Search and Random Query

TL;DR

This study compares BFS and Random Query mechanisms in evolving modular robot morphologies, showing BFS's superior efficiency and impact on diversity, morphology, and performance in different evolutionary frameworks.

Contribution

It introduces a comparative analysis of query mechanisms in brain-body co-evolution, highlighting BFS's advantages over Random Query in modular robot evolution.

Findings

BFS leads to higher robot performance and diversity.

BFS converges faster to superior designs in Lamarckian systems.

Initially higher diversity with BFS decreases faster in Lamarckian systems.

Abstract

Evolutionary robotics offers a powerful framework for designing and evolving robot morphologies, particularly in the context of modular robots. However, the role of query mechanisms during the genotype-to-phenotype mapping process has been largely overlooked. This research addresses this gap by conducting a comparative analysis of query mechanisms in the brain-body co-evolution of modular robots. Using two different query mechanisms, Breadth-First Search (BFS) and Random Query, within the context of evolving robot morphologies using CPPNs and robot controllers using tensors, and testing them in two evolutionary frameworks, Lamarckian and Darwinian systems, this study investigates their influence on evolutionary outcomes and performance. The findings demonstrate the impact of the two query mechanisms on the evolution and performance of modular robot bodies, including morphological intelligence, diversity, and morphological traits. This study suggests that BFS is both more effective and efficient in producing highly performing robots. It also reveals that initially, robot diversity was higher with BFS compared to Random Query, but in the Lamarckian system, it declines faster, converging to superior designs, while in the Darwinian system, BFS led to higher end-process diversity.