Cross-Modal Surface Material Retrieval Using Discriminant Adversarial Learning

The surface properties of an object play a vital role in the tasks of robotic manipulation or interaction with its surrounding environment. Tactile sensing can provide rich information about the surface properties of an object through physical contact. Hence, how to convey and interpret the tactile information to the user is a significant problem during the human-machine interaction. To this end, a visual-tactile cross-modal retrieval framework is proposed for perceptual estimation by associating tactile information to visual information of material surfaces. Namely, we can use tactile information of an unknown material surface to retrieve perceptually similar surfaces from an available surface visual sample set. For the proposed framework, we develop a discriminant adversarial learning method, which incorporates intramodal discriminant, cross-modal correlation, and intermodal consistency into a deep learning network for common feature representation learning. Experimental results on the publicly available data set show that the proposed framework and the method are effective.