Gene interactions
This study reveals how gene interactions influence the traits and behaviors of robots designed using bio-inspired algorithms. Through the application of measurement techniques from experimental biology to artificial gene regulatory networks, it is shown that epistasis--a non-additive effect of gene interaction--can be shaped by evolutionary processes in artificial systems, while influencing the quality of the robot traits. These insights offer a path toward improving the explainability, reliability and safety of robotic systems.
Costs of Plasticity
Phenotypic plasticity is usually defined as a property of individual genotypes to produce different phenotypes when exposed to different environmental conditions. While the benefits of plasticity for adaptation are well established, the costs associated with plasticity remain somewhat obscure. Understanding both why and how these costs arise could help us explain and predict the behavior of living creatures as well as allow the design of more adaptable robotic systems. The hypothesis put forward in this study is that the potential benefits of plasticity might be undermined by the genetic costs related to plasticity itself. The results suggest that this hypothesis is true, while further research is needed to guarantee that the observed effects unequivocally derive from genetic costs and not from some other (unforeseen) mechanism related to plasticity.
Animal-like emergent robots
In this work, we consider evolutionary robot systems where both the “bodies” and the “brains”, i.e., the morphologies and the controllers, of the robots are evolvable. The main goal was to investigate how the evolved morphological features change when using different criteria for selection, e.g., speed and morphological diversity. Results showed the emergency of animal-like gaits and morphologies. Notably, these animal-like behaviors and morphologies were not in any way intentionally introduced into the system: they emerged from the application of general natural principles, e.g., natural selection. What does this tell us about the natural evolution principles being applied?
Environmental regulation
Evolutionary robot systems are usually affected by the properties of the environment indirectly through selection. In this paper, we present and investigate a system where the environment also has a direct effect—through regulation. We propose a novel robot encoding method where a genotype encodes multiple possible phenotypes, and the incarnation of a robot depends on the environmental conditions taking place in a determined moment of its life. We provide an empirical proof-of-concept, and the analysis of the experimental results shows that environmental regulation improves adaptation (task performance) while leading to different evolved morphologies, controllers, and behavior.
Changing environments
This work studies the effects of changing environments on the evolution of bodies and brains of modular robots. Our results indicate that environmental history has a long lasting impact on the evolved robot properties. We show that if the environment gradually changes from type A to type B, then the evolved morphological and behavioral properties are very different from those evolving in a type B environment directly. That is, we observe some sort of “genetic memory”. Furthermore, we show that gradually introducing a difficult environment helps to reach fitness levels that are higher than those obtained under those difficult conditions directly. Finally, we also demonstrate that robots evolved in gradually changing environments are more robust.
Representation bias
Genetic encodings and their particular properties are known to have a strong influence on the success of evolutionary systems. However, the literature has widely focused on studying the effects that encodings have on performance, i.e., fitness-oriented studies. Notably, this anchoring of the literature to performance is limiting, considering that performance provides bounded information about the behavior of a robot system. In this paper, we investigate how genetic encodings constrain the space of robot phenotypes and robot behavior. In summary, we demonstrate how two generative encodings of different nature lead to very different robots and discuss these differences.