Learning momentum: online performance enhancement for reactive systems

The authors describe a reactive robotic control system which incorporates aspects of machine learning to improve the system´s ability to navigate successfully in unfamiliar environments. This system overcomes limitations of completely reactive systems by exercising online performance enhancement without the need for high-level planning. The goal of the learning system is to give the autonomous robot the ability to adjust the scheme control parameters in an unstructured dynamic environment. The results of a successful implementation that learns to navigate out of a box canyon are presented. This system never resorts to a high-level planner, but instead learns continuously by adjusting gains based on the progress made so far. The system is successful because it is able to improve its performance in reaching a goal in a previously unfamiliar and dynamic world