  Tactile sensors are believed to be essential in robotic manipulation, and
prior works often rely on experts to reason the sensor feedback and design a
controller. With the recent advancement in data-driven approaches, complicated
manipulation can be realised, but an accurate and efficient tactile simulation
is necessary for policy training. To this end, we present an approach to model
a commonly used pressure sensor array in simulation and to train a
tactile-based manipulation policy with sim-to-real transfer in mind. Each taxel
in our model is represented as a mass-spring-damper system, in which the
parameters are iteratively identified as plausible ranges. This allows a policy
to be trained with domain randomisation which improves its robustness to
different environments. Then, we introduce encoders to further align the
critical tactile features in a latent space. Finally, our experiments answer
questions on tactile-based manipulation, tactile modelling and sim-to-real
performance.
