  We present a Garment Similarity Network (GarNet) that learns geometric and
physical similarities between known garments by continuously observing a
garment while a robot picks it up from a table. The aim is to capture and
encode geometric and physical characteristics of a garment into a manifold
where a decision can be carried out, such as predicting the garment's shape
class and its visually perceived weight. Our approach features an early stop
strategy, which means that GarNet does not need to observe a garment being
picked up from a crumpled to a hanging state to make a prediction. In our
experiments, we find that GarNet achieves prediction accuracies of 92% for
shape classification and 95.5% for predicting weights and advances state-of-art
approaches by 21% for shape classification.
