title: Variants of guided self-organization for robot control.
abstract: Autonomous robots can generate exploratory behavior by self-organization of the sensorimotor loop. We show that the behavioral manifold that is covered in this way can be modified in a goal-dependent way without reducing the self-induced activity of the robot. We present three strategies for guided self-organization, namely by using external rewards, a problem-specific error function, or assumptions about the symmetries of the desired behavior. The strategies are analyzed for two different robots in a physically realistic simulation.
authors: Bernstein Center for Computational Neuroscience and Max Planck Institute for Dynamics and Self-Organization, Bunsenstr. 10, 37073, Göttingen, Germany. martius@mis.mpg.de
authors: Georg
authors: G
authors: Martius
authors: J Michael
authors: JM
authors: Herrmann
mesh_terms: D001519:Behavior; D007858:Learning; D016571:Neural Networks, Computer; D012371:Robotics / Q000379:methods* / Q000458:organization & administration; D013663:Teaching