Short-term memory mechanisms in neural network learning of robot navigation tasks: a case study

Abstract

This paper reports results of an investigation on the degree of influence of short-term memory mechanisms on the performance of neural classifiers when applied to robot navigation tasks. In particular, we deal with the well-known strategy of navigating by “wall-following”. For this purpose, four standard neural architectures (Logistic Perceptron, Multilayer Perceptron, Mixture of Experts and Elman network) are used to associate different spatiotemporal sensory input patterns with four predetermined action categories. All stages of the experiments - data acquisition, selection and training of the architectures in a simulator and their execution on a real mobile robot - are described. The obtained results suggest that the wall-following task, formulated as a pattern classification problem, is nonlinearly separable, a result that favors the MLP network if no memory of input patters are taken into account. If short-term memory mechanisms are used, then even a linear network is able to perform the same task successfully.