Dynamic regulation of Hebb learning by artificial neuromodulators in mobile autonomous robots

We investigate key components of a dynamic neurocontroller changing its internal structure enabling "lifetime" learning of a mobile autonomous robot. The behavioral change of the robot is linked to inputs from the environment that cause the emission of artificial neuromodulators (ANMs) in the robot\´s neurocontroller. In its simplest form an outside teacher (human or machine) constantly evaluates the robot\´s actions by transmitting positive or negative feedback signals to the robot initiating the internal changes. The focus of investigations is put on the mechanisms of the interaction of teaching input and structural changes. A well-known concept for this interaction is Hebbian learning, which is regulated by ANMs in the presented approach. In extension to related work in evolutionary robotics (ER), we analyze important details of robotic (ontogenetic) learning by experiments measuring the ability of robots to learn simple tasks in a simulated environment without employing evolution. Specifically, we are interested in the comparison of Hebb learning variants, and the crucial question of the correct interpretation of reward or punishment signals by the robot.