AUTHOR=Aaron Eric , Hawthorne-Madell Joshua , Livingston Ken , Long John H. 

TITLE=Morphological Evolution: Bioinspired Methods for Analyzing Bioinspired Robots

JOURNAL=Frontiers in Robotics and AI

VOLUME=Volume 8 - 2021

YEAR=2022

URL=https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2021.717214

DOI=10.3389/frobt.2021.717214

ISSN=2296-9144

ABSTRACT=To fully understand the complex evolutionary dynamics that can underlie complex morphologies, analyses cannot stop at selection: It is essential to investigate the roles, impacts, and interactions of multiple processes that can drive evolutionary outcomes. The challenges of undertaking such analyses have affected both evolutionary biologists and evolutionary roboticists, with their common interests in studies of complex morphologies. In this paper, we present a collection of analytical techniques from evolutionary biology, including selection gradient analysis and morphospace walks, and we demonstrate their applicability to complex robot morphologies in analyses of three driver mechanisms: randomness (genetic mutation), development (an explicitly implemented G-P map), and selection. In particular, we applied these analytical techniques to evolved populations of simulated biorobots--i.e., embodied robots designed specifically as models of biological systems, for the testing of  biological hypotheses--and we present a variety of results, including analyses that do all of the following: illuminate different evolutionary dynamics for different classes of morphological traits; illustrate how the traits targeted by selection can vary based on the likelihood of random genetic mutation; demonstrate that morphology only partially explains the variance in fitness in our biorobots; and suggest that bias in the implementation of developmental processes (even if unintended by algorithm designers) can drive morphological evolution. This application to complex robot systems shows that, when combined, the distinct but complementary analytical approaches discussed in this paper can enable insight into evolutionary driver processes beyond selection and thereby deepen our understanding of the evolution of robotic morphologies.