  The labyrinth game is a simple yet challenging platform, not only for humans
but also for control algorithms and systems. The game is easy to understand but
still very hard to master. From a system point of view, the ball behaviour is
in general easy to model but close to the obstacles there are severe
non-linearities. Additionally, the far from flat surface on which the ball
rolls provides for changing dynamics depending on the ball position. The
general dynamics of the system can easliy be handled by traditional automatic
control methods. Taking the obstacles and uneaven surface into accout would
require very detailed models of the system. A simple deterministic control
algorithm is combined with a learning control method. The simple control method
provides initial training data. As the learning method is trained, the system
can learn from the results of its own actions and the performance improves well
beyond the performance of the initial controller. A vision system and image
analysis is used to estimate the ball position while a combination of a PID
controller and a learning controller based on LWPR is used to learn to navigate
the ball through the maze.
