  This work investigates uncertainty-aware deep learning (DL) in tactile
robotics based on a general framework introduced recently for robot vision. For
a test scenario, we consider optical tactile sensing in combination with DL to
estimate the edge pose as a feedback signal to servo around various 2D test
objects. We demonstrate that uncertainty-aware DL can improve the pose
estimation over deterministic DL methods. The system estimates the uncertainty
associated with each prediction, which is used along with temporal coherency to
improve the predictions via a Kalman filter, and hence improve the tactile
servo control. The robot is able to robustly follow all of the presented
contour shapes to reduce not only the error by a factor of two but also smooth
the trajectory from the undesired noisy behaviour caused by previous
deterministic networks. In our view, as the field of tactile robotics matures
in its use of DL, the estimation of uncertainty will become a key component in
the control of physically interactive tasks in complex environments.
