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

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 Percep-tron, 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.