  Tactile sensing is critical for robotic grasping and manipulation of objects
under visual occlusion. However, in contrast to simulations of robot arms and
cameras, current simulations of tactile sensors have limited accuracy, speed,
and utility. In this work, we develop an efficient 3D finite element method
(FEM) model of the SynTouch BioTac sensor using an open-access, GPU-based
robotics simulator. Our simulations closely reproduce results from an
experimentally-validated model in an industry-standard, CPU-based simulator,
but at 75x the speed. We then learn latent representations for simulated BioTac
deformations and real-world electrical output through self-supervision, as well
as projections between the latent spaces using a small supervised dataset.
Using these learned latent projections, we accurately synthesize real-world
BioTac electrical output and estimate contact patches, both for unseen contact
interactions. This work contributes an efficient, freely-accessible FEM model
of the BioTac and comprises one of the first efforts to combine
self-supervision, cross-modal transfer, and sim-to-real transfer for tactile
sensors.
