  Adding tactile sensors to a robotic system is becoming a common practice to
achieve more complex manipulation skills than those robotics systems that only
use external cameras to manipulate objects. The key of tactile sensors is that
they provide extra information about the physical properties of the grasping.
In this paper, we implemented a system to predict and quantify the rotational
slippage of objects in hand using the vision-based tactile sensor known as
Digit. Our system comprises a neural network that obtains the segmented contact
region (object-sensor), to later calculate the slippage rotation angle from
this region using a thinning algorithm. Besides, we created our own tactile
segmentation dataset, which is the first one in the literature as far as we are
concerned, to train and evaluate our neural network, obtaining results of 95%
and 91% in Dice and IoU metrics. In real-scenario experiments, our system is
able to predict rotational slippage with a maximum mean rotational error of 3
degrees with previously unseen objects. Thus, our system can be used to prevent
an object from falling due to its slippage.
