Fast Connectionist Learning for Trailer Backing using a Real Robot

This paper presents the application of a connectionist control-learning system to an autonomous minirobot. The system's design is severely constrained by the computing power and memory available on board the mini-robot and the on-board training time is greatly limited by the short life of the battery. The system is capable of rapid unsupervised learning of output responses in temporal domains through the use of eligibility traces and data sharing within topologically defined neighborhoods. 1 Introduction Control-learning in autonomous robotic systems provides many challenges. The learning system must be robust enough to overcome the problems of noisy input data and uncertain interactions between motor commands and effects in the world, be compact enough to fit into available on-board memory, and be able to give responses in real time. The challenges are even greater if the learning system must acquire its proficiency during a short demonstration period consisting of a small number of..