Testing Biological Hypotheses with Embodied Robots: Adaptations, Accidents, and By-Products in the Evolution of Vertebrates - Roberts, Sonia F.; Hirokawa, Jonathan; Rosenblum, Hannah G.; Sakhtah, Hassan; Gutierrez, Andres A.; Porter, Marianne E.; Long, John H. (Frontiers in Robotics and AI, 2014)
Evolutionary robotics allows biologists to test hypotheses about extinct animals. We modeled some of the first vertebrates, jawless fishes, in order to study the evolution of the trait after which vertebrates are named: vertebrae. We tested the hypothesis that vertebrae are an adaptation for enhanced feeding and fleeing performance. We created a population of autonomous embodied robots, Preyro, in which the number of vertebrae, N, were free to evolve. In addition, two other traits, the span of the caudal fin, b, and the predator detection threshold, ζ, a proxy for the lateral line sensory system, were also allowed to evolve. These three traits were chosen because they evolved early in vertebrates, are all potentially important in feeding and fleeing, and vary in form among species. Preyro took on individual identities in a given generation as defined by the population’s six diploid genotypes, Gi. Each Gi was a 3-tuple, with each element an integer specifying N, b, and, ζ. The small size of the population allowed for genetic drift to operate in concert with random mutation and mating; the presence of these mechanisms of chance provided an opportunity for N to evolve by accident. The presence of three evolvable traits provided an opportunity for direct selection on b and/or ζ to evolve N as a by-product linked trait correlation. In selection trials, different Gi embodied in Preyro attempted to feed at a light source and then flee to avoid a predator robot in pursuit. The fitness of each Gi was calculated from five different types of performance: speed, acceleration, distance to the light, distance to the predator, and the number of predator escapes initiated. In each generation, we measured the selection differential, the selection gradient, the strength of chance, and the indirect correlation selection gradient. These metrics allowed us to understand the relative contributions of the three mechanisms: direct selection, chance, and indirect selection.