Quality and Diversity in Evolutionary Modular Robotics

TL;DR

This paper compares different evolutionary algorithms for evolving control and morphology in modular robotics, finding that Quality Diversity algorithms excel at generating diverse, high-performing solutions and filling all niches.

Contribution

It demonstrates that Quality Diversity algorithms outperform traditional and multi-objective methods in evolving diverse, high-quality modular robotic solutions.

Findings

Quality Diversity algorithms fill all niches with high-performing solutions.

All algorithms can evolve high-performing individuals.

QDA is more effective at promoting morphological diversity.

Abstract

In Evolutionary Robotics a population of solutions is evolved to optimize robots that solve a given task. However, in traditional Evolutionary Algorithms, the population of solutions tends to converge to local optima when the problem is complex or the search space is large, a problem known as premature convergence. Quality Diversity algorithms try to overcome premature convergence by introducing additional measures that reward solutions for being different while not necessarily performing better. In this paper we compare a single objective Evolutionary Algorithm with two diversity promoting search algorithms; a Multi-Objective Evolutionary Algorithm and MAP-Elites a Quality Diversity algorithm, for the difficult problem of evolving control and morphology in modular robotics. We compare their ability to produce high performing solutions, in addition to analyze the evolved morphological diversity. The results show that all three search algorithms are capable of evolving high performing individuals. However, the Quality Diversity algorithm is better adept at filling all niches with high-performing solutions. This confirms that Quality Diversity algorithms are well suited for evolving modular robots and can be an important means of generating repertoires of high performing solutions that can be exploited both at design- and runtime.