  Deep learning and reinforcement learning methods have been shown to enable
learning of flexible and complex robot controllers. However, the reliance on
large amounts of training data often requires data collection to be carried out
in simulation, with a number of sim-to-real transfer methods being developed in
recent years. In this paper, we study these techniques for tactile sensing
using the TacTip optical tactile sensor, which consists of a deformable tip
with a camera observing the positions of pins inside this tip. We designed a
model for soft body simulation which was implemented using the Unity physics
engine, and trained a neural network to predict the locations and angles of
edges when in contact with the sensor. Using domain randomisation techniques
for sim-to-real transfer, we show how this framework can be used to accurately
predict edges with less than 1 mm prediction error in real-world testing,
without any real-world data at all.
