Acquiring mobile robot behaviors by learning trajectory velocities with multiple FAM matrices

Abstract

We describe an unsupervised robot learning method which is based on the robot learning a mapping between sensors and trajectory velocities. This enables the robot to acquire object avoidance, wall following and goal seeking behaviors simultaneously without incurring the credit assignment problem. To improve the robot's perception and behaviors we provide the robot with 7 fuzzy associative matrices (FAMs) so that sensors can be mapped to each trajectory independently. We provide results demonstrating how a mobile robot equipped with 16 sonar sensors is able to achieve improved perception and behaviors by using 7 FAMs to map sensors to trajectories.