title: Coevolution of active vision and feature selection.
abstract: We show that complex visual tasks, such as position- and size-invariant shape recognition and navigation in the environment, can be tackled with simple architectures generated by a coevolutionary process of active vision and feature selection. Behavioral machines equipped with primitive vision systems and direct pathways between visual and motor neurons are evolved while they freely interact with their environments. We describe the application of this methodology in three sets of experiments, namely, shape discrimination, car driving, and robot navigation. We show that these systems develop sensitivity to a number of oriented, retinotopic, visual-feature-oriented edges, corners, height, and a behavioral repertoire to locate, bring, and keep these features in sensitive regions of the vision system, resembling strategies observed in simple insects.
authors.affiliation: Autonomous Systems Lab, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland. Dario.Floreano@epfl.ch
authors.forename: Dario
authors.initials: D
authors.lastname: Floreano
authors.forename: Toshifumi
authors.initials: T
authors.lastname: Kato
authors.forename: Davide
authors.initials: D
authors.lastname: Marocco
authors.forename: Eric
authors.initials: E
authors.lastname: Sauser
mesh_terms: D000465:Algorithms; D000818:Animals; D001334:Automobile Driving; D005075:Biological Evolution; D004192:Discrimination, Psychological / Q000502:physiology*; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D010775:Photic Stimulation; D012371:Robotics; D014785:Vision, Ocular / Q000502:physiology*; D014795:Visual Pathways / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology*