  Most current works in Sim2Real learning for robotic manipulation tasks
leverage camera vision that may be significantly occluded by robot hands during
the manipulation. Tactile sensing offers complementary information to vision
and can compensate for the information loss caused by the occlusion. However,
the use of tactile sensing is restricted in the Sim2Real research due to no
simulated tactile sensors being available. To mitigate the gap, we introduce a
novel approach for simulating a GelSight tactile sensor in the commonly used
Gazebo simulator. Similar to the real GelSight sensor, the simulated sensor can
produce high-resolution images by an optical sensor from the interaction
between the touched object and an opaque soft membrane. It can indirectly sense
forces, geometry, texture and other properties of the object and enables
Sim2Real learning with tactile sensing. Preliminary experimental results have
shown that the simulated sensor could generate realistic outputs similar to the
ones captured by a real GelSight sensor. All the materials used in this paper
are available at https://danfergo.github.io/gelsight-simulation.
