Abstract
To fully understand the evolution of complex morphologies, analyses cannot stop at selection: It is essential to investigate the roles and interactions of multiple processes that drive evolutionary outcomes. The challenges of undertaking such analyses have affected both evolutionary biologists and evolutionary roboticists, with their common interests in complex morphologies. In this paper, we present analytical techniques from evolutionary biology, selection gradient analysis and morphospace walks, and we demonstrate their applicability to robot morphologies in analyses of three evolutionary mechanisms: randomness (genetic mutation), development (an explicitly implemented genotype-to-phenotype map), and selection. In particular, we applied these analytical techniques to evolved populations of simulated biorobots—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 selection on two selected sets of morphological traits only partially explains the variance in fitness in our biorobots; and suggest that biases in developmental processes could partially explain evolutionary dynamics of morphology. When combined, the complementary analytical approaches discussed in this paper can enable insight into evolutionary processes beyond selection and thereby deepen our understanding of the evolution of robotic morphologies.
Highlights
For evolutionary roboticists, grand challenges target finding original designs, closing the reproduction loop in physical robots, and allowing for open-ended evolution of physical robots in real environments (Eiben, 2014), which echo the grand challenge from organismal biologists to integrate the analysis of physical and biological systems in order to understand complexity (Schwenk et al, 2009)
The processes that underlie the evolution of complex morphologies are often themselves complex, for Morphological Evolution: Bioinspired Analysis both biological organisms and evolved robots; a deep understanding of evolved morphologies requires techniques to analyze the relevant underlying processes—to answer questions about what morphological forms occur over generational time, and how and why they occur
The evolution of robotic morphology is open-ended and complicated, but details can be exposed and investigated by aptly focused analytical methods: straightforward accounting illuminates the separate fates of different traits; selection gradient analysis uncovers how traits are targeted by selection and how that targeting varies over generational time; and morphospace walks can explore how randomness, development, and selection interact in morphological evolution
Summary
Grand challenges target finding original designs, closing the reproduction loop in physical robots, and allowing for open-ended evolution of physical robots in real environments (Eiben, 2014), which echo the grand challenge from organismal biologists to integrate the analysis of physical and biological systems in order to understand complexity (Schwenk et al, 2009). From this perspective, morphology matters for embodied robots in the same ways that it matters for biological organisms: It permits and constrains individual behavior, and it shapes properties of populations that matter for evolution (Hochner, 2013; Cappelle, et al, 2016). The biorobots in this paper (first described in Aaron and Long, 2021; Hawthorne-Madell et al, 2021) have bioinspired genomic foundations that result in bioinspired morphologies, and they are digitally simulated and embodied in the sense that they operate according to physical rules, with fitness determined by performance on a simple locomotion task: distance traveled in an empty, flat, terrestrial environment
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