  Tactile sensors can provide detailed contact in-formation that can facilitate
robots to perform dexterous, in-hand manipulation tasks. One of the primitive
but important tasks is surface following that is a key feature for robots while
exploring unknown environments or workspace of inaccurate modeling. In this
paper, we propose a novel end-to-end learning strategy, by directly mapping the
raw tactile data acquired from a GelSight tactile sensor to the motion of the
robot end-effector.Experiments on a KUKA youBot platform equipped with
theGelSight sensor show that 80% of the actions generated by a fully trained
SFDQN model are proper surface following actions; the autonomous surface
following test also indicates that the proposed solution works well on a test
surface.
