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We present a Garment Similarity Network (GarNet) that learns geometric andphysical similarities between known garments by continuously observing agarment while a robot picks it up from a table. The aim is to capture andencode geometric and physical characteristics of a garment into a manifoldwhere a decision can be carried out, such as predicting the garment's shapeclass and its visually perceived weight. Our approach features an early stopstrategy, which means that GarNet does not need to observe a garment beingpicked up from a crumpled to a hanging state to make a prediction. In ourexperiments, we find that GarNet achieves prediction accuracies of 92% forshape classification and 95.5% for predicting weights and advances state-of-artapproaches by 21% for shape classification.

GarNet: A Continuous Robot Vision Approach for Predicting Shapes and Visually Perceived Weights of Garments