  Simulators perform an important role in prototyping, debugging and
benchmarking new advances in robotics and learning for control. Although many
physics engines exist, some aspects of the real-world are harder than others to
simulate. One of the aspects that have so far eluded accurate simulation is
touch sensing. To address this gap, we present TACTO -- a fast, flexible and
open-source simulator for vision-based tactile sensors. This simulator allows
to render realistic high-resolution touch readings at hundreds of frames per
second, and can be easily configured to simulate different vision-based tactile
sensors, including GelSight, DIGIT and OmniTact. In this paper, we detail the
principles that drove the implementation of TACTO and how they are reflected in
its architecture. We demonstrate TACTO on a perceptual task, by learning to
predict grasp stability using touch from 1 million grasps, and on a marble
manipulation control task. We believe that TACTO is a step towards the
widespread adoption of touch sensing in robotic applications, and to enable
machine learning practitioners interested in multi-modal learning and control.
TACTO is open-source at https://github.com/facebookresearch/tacto.
