The Evolution of Controller-Free Molecular Motors from Spatial Constraints

Abstract

Locomotion of robotic and virtual agents is a challenging task requiring the control of several degrees of freedom as well as the coordination of multiple subsystems. Traditionally, it is engineered by top-down design and finetuning of the agent’s morphology and controller. A relatively recent trend in fields such as evolutionary robotics, computer animation and artificial life has been the coevolution and mutual adaptation of the morphology and controller in computational agent models. However, the controller is generally modeled as a complex system, often a neural or gene regulatory network. In the present study, inspired by molecular biology and based on normal modal analysis, we formulate a behavior-finding framework for the design of bipedal agents that are able to walk along a filament and have no explicit control system. Instead, agents interact with their environment in a purely reactive way. A simple mutation operator, based on physical relaxation, is used to drive the evolutionary search. Results show that gait patterns can be evolutionarily engineered from the spatial interaction between precisely tuned morphologies and the environment.