Identifying contact formations from force signals: a comparison of fuzzy and neural network classifiers

Abstract

In this paper, we present and compare two methods of identifying single-ended contact formations from force sensor patterns. The purpose is to achieve robot programming by demonstration. Instead of using geometric models of the workpieces, both methods use force sensor signals only. In the first method, fuzzy logic is used to model the patterns in the force signals. Membership functions are generated automatically from training data and then used by the fuzzy classifier. In the second method, a neural network architecture is used to learn the mapping from force signals to contact formation class. Experimental results are presented for both the fuzzy and neural network classifiers, and the results are compared. In some cases, the fuzzy classifier has better performance, and in other cases, the neural net classifier is better. The results are discussed, and, finally, a training modification is presented which dramatically improves the performance of the inadequate neural net classifiers.