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The labyrinth game is a simple yet challenging platform, not only for humansbut also for control algorithms and systems. The game is easy to understand butstill very hard to master. From a system point of view, the ball behaviour isin general easy to model but close to the obstacles there are severenon-linearities. Additionally, the far from flat surface on which the ballrolls provides for changing dynamics depending on the ball position. Thegeneral dynamics of the system can easliy be handled by traditional automaticcontrol methods. Taking the obstacles and uneaven surface into accout wouldrequire very detailed models of the system. A simple deterministic controlalgorithm is combined with a learning control method. The simple control methodprovides initial training data. As the learning method is trained, the systemcan learn from the results of its own actions and the performance improves wellbeyond the performance of the initial controller. A vision system and imageanalysis is used to estimate the ball position while a combination of a PIDcontroller and a learning controller based on LWPR is used to learn to navigatethe ball through the maze.

Combining Vision, Machine Learning and Automatic Control to Play the Labyrinth Game