Selection pressure shapes epistatic gene interactions in the evolution of robots

The evolution of robots has promoted both technological development and biological inquiry, but little to no effort has been placed on exploring gene interactions within evolving artificial genetic encodings. Studying gene interactions is fundamental to understandinghow biological systems function, and the same extends to artificial evolvable systems. As autonomous systems become more complex, reliance on black-box encodings where it is unclear how the genome produces phenotypes poses limitations for efficacy, explainability as well as safety. This study investigates epistatic gene interactions---a non-additive effect of two gene knockouts on a trait--in the evolution of robots encoded with artificial Gene Regulatory Networks (GRNs). We evolve robotic populations in a physics-based simulation and apply techniques from experimental biology to identify and quantify the evolution of epistasis in these robot systems. Our analysis reveals that selection pressure acts upon epistasis and also shapes its distribution differently across distinct GRN designs. These findings establish that epistasis can be an evolvable property in robotic systems and that tuning encoding characteristics can directly influence trait expression. By uncovering how gene interactions underpin morphology and behavior, this work constitutes a stepping stone toward more explainable--and thus safer--artificial genetic encodings. A video summarizing the results is available in the results section.