  Recently, tactile sensing has attracted great interest in robotics,
especially for facilitating exploration of unstructured environments and
effective manipulation. A detailed understanding of the surface textures via
tactile sensing is essential for many of these tasks. Previous works on texture
recognition using camera based tactile sensors have been limited to treating
all regions in one tactile image or all samples in one tactile sequence
equally, which includes much irrelevant or redundant information. In this
paper, we propose a novel Spatio-Temporal Attention Model (STAM) for tactile
texture recognition, which is the very first of its kind to our best knowledge.
The proposed STAM pays attention to both spatial focus of each single tactile
texture and the temporal correlation of a tactile sequence. In the experiments
to discriminate 100 different fabric textures, the spatially and temporally
selective attention has resulted in a significant improvement of the
recognition accuracy, by up to 18.8%, compared to the non-attention based
models. Specifically, after introducing noisy data that is collected before the
contact happens, our proposed STAM can learn the salient features efficiently
and the accuracy can increase by 15.23% on average compared with the CNN based
baseline approach. The improved tactile texture perception can be applied to
facilitate robot tasks like grasping and manipulation.
