Classification of clothing using interactive perception

We present a system for automatically extracting and classifying items in a pile of laundry. Using only visual sensors, the robot identifies and extracts items sequentially from the pile. When an item has been removed and isolated, a model is captured of the shape and appearance of the object, which is then compared against a database of known items. The classification procedure relies upon silhouettes, edges, and other low-level image measurements of the articles of clothing. The contributions of this paper are a novel method for extracting articles of clothing from a pile of laundry and a novel method of classifying clothing using interactive perception. Experiments demonstrate the ability of the system to efficiently classify and label into one of six categories (pants, shorts, short-sleeve shirt, long-sleeve shirt, socks, or underwear). These results show that, on average, classification rates using robot interaction are 59% higher than those that do not use interaction.