Ontogenetic Teaching of Mobile Autonomous Robots with
Dynamic Neurocontrollers
After a brief survey of work dealing with dynamic neurocontrollers changing their
internal structure during the "lifetime" of a mobile autonomous robot, we present
experiments employing a standard sensor-motor neurocontroller with self-adapting
weights. The change of behavior 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.
Helmut A. Mayer
Last modified: Feb 1 2005