  Material recognition enables robots to incorporate knowledge of material
properties into their interactions with everyday objects. For example, material
recognition opens up opportunities for clearer communication with a robot, such
as "bring me the metal coffee mug", and recognizing plastic versus metal is
crucial when using a microwave or oven. However, collecting labeled training
data with a robot is often more difficult than unlabeled data. We present a
semi-supervised learning approach for material recognition that uses generative
adversarial networks (GANs) with haptic features such as force, temperature,
and vibration. Our approach achieves state-of-the-art results and enables a
robot to estimate the material class of household objects with ~90% accuracy
when 92% of the training data are unlabeled. We explore how well this approach
can recognize the material of new objects and we discuss challenges facing
generalization. To motivate learning from unlabeled training data, we also
compare results against several common supervised learning classifiers. In
addition, we have released the dataset used for this work which consists of
time-series haptic measurements from a robot that conducted thousands of
interactions with 72 household objects.
