Identifying single-ended contact formations from force sensor patterns

We present two methods of rapidly (less than 1 ms) identifying contact formations from force sensor patterns, including friction and measurement uncertainty. Both principally use force signals instead of positions and detailed geometric models. First, fuzzy sets are used to model patterns and sensor uncertainty; membership functions are generated automatically from training data. Second, a neural network is used to generate confidence levels for each contact formation. Experimental results are presented for both classifiers, showing excellent results. New insights into the data sets are discussed, and a modified training method is presented that further improves the performance. The classification techniques are discussed in the context of robot programming by demonstration.