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Humans represent and discriminate the objects in the same category usingtheir properties, and an intelligent robot should be able to do the same. Inthis paper, we build a robot system that can autonomously perceive the objectproperties through touch. We work on the common object category of clothing.The robot moves under the guidance of an external Kinect sensor, and squeezesthe clothes with a GelSight tactile sensor, then it recognizes the 11properties of the clothing according to the tactile data. Those propertiesinclude the physical properties, like thickness, fuzziness, softness anddurability, and semantic properties, like wearing season and preferred washingmethods. We collect a dataset of 153 varied pieces of clothes, and conduct 6616robot exploring iterations on them. To extract the useful information from thehigh-dimensional sensory output, we applied Convolutional Neural Networks (CNN)on the tactile data for recognizing the clothing properties, and on the Kinectdepth images for selecting exploration locations. Experiments show that usingthe trained neural networks, the robot can autonomously explore the unknownclothes and learn their properties. This work proposes a new framework foractive tactile perception system with vision-touch system, and has potential toenable robots to help humans with varied clothing related housework.

Active Clothing Material Perception using Tactile Sensing and Deep Learning