The truck backer-upper: an example of self-learning in neural networks

Neural networks can be used to solve highly nonlinear control problems. A two-layer neural network containing 26 adaptive neural elements has learned to back up a computer-simulated trailer truck to a loading dock, even when initially jackknifed. It is not yet known how to design a controller to perform this steering task. Nevertheless, the neural net was able to learn of its own accord to do this, regardless of initial conditions. Experience gained with the truck backer-upper should be applicable to a wide variety of nonlinear control problems.>