An Application of Category-Theoretic Design Methods to the Control of a Simulated Robot

The use of neural network architectures has historically presented a challenge to engineers. Problem domains could be "learned", but the acquired knowledge could be extracted only under limited circumstances. Healy and Caudell\´s application of category theory has been shown to improve both architecture design and performance. This paper reports on the application of category theory to the design of a simulated robot control system, where the neural network controller is constructed based upon a desired conceptual ontology. Three experiments then explore the implications of this approach on the prediction and improvement of robot performance.