Navigation towards a goal position: from reactive to generalised learned control

The task of navigating to a target position in space is a fairly common task for a mobile robot. It is desirable that this task is performed even in previously unknown environments. One reactive architecture explored before addresses this challenge by denning a hand-coded coordination of primitive behaviours, encoded by the Potential Fields method. Our first approach to improve the performance of this architecture adds a learning step to autonomously find the best way to coordinate primitive behaviours with respect to an arbitrary performance criterion. Because of the limitations presented by the Potential Fields method, especially in relation to non-convex obstacles, we are investigating the use of Relational Reinforcement Learning as a method to not only learn to act in the current environment, but also to generalise prior knowledge to the current environment in order to achieve the goal more quickly in a non-convex structured environment. We show the results of our previous efforts in reaching goal positions along with our current research on generalised approaches.