The Role of Morphological Variation in Evolutionary Robotics: Maximizing Performance and Robustness

TL;DR

This paper investigates how morphological variations influence the evolution of robot controllers, revealing that certain variations enhance robustness and performance, and providing a method to measure their impact for better evolutionary outcomes.

Contribution

Introduces a method to measure the impact of morphological variations on evolutionary robotics and analyzes their effects on robustness and performance.

Findings

Evolutionary algorithms tolerate high-impact morphological variations.

Variations affecting actions are better tolerated than those affecting initial state.

Multiple evaluations of fitness are not always beneficial.

Abstract

Exposing an Evolutionary Algorithm that is used to evolve robot controllers to variable conditions is necessary to obtain solutions which are robust and can cross the reality gap. However, we do not yet have methods for analyzing and understanding the impact of the varying morphological conditions which impact the evolutionary process, and therefore for choosing suitable variation ranges. By morphological conditions, we refer to the starting state of the robot, and to variations in its sensor readings during operation due to noise. In this article, we introduce a method that permits us to measure the impact of these morphological variations and we analyze the relation between the amplitude of variations, the modality with which they are introduced, and the performance and robustness of evolving agents. Our results demonstrate that (i) the evolutionary algorithm can tolerate morphological variations which have a very high impact, (ii) variations affecting the actions of the agent are tolerated much better than variations affecting the initial state of the agent or of the environment, and (iii) improving the accuracy of the fitness measure through multiple evaluations is not always useful. Moreover, our results show that morphological variations permit generating solutions which perform better both in varying and non-varying conditions.