  Camera-based tactile sensors are emerging as a promising inexpensive solution
for tactile-enhanced manipulation tasks. A recently introduced FingerVision
sensor was shown capable of generating reliable signals for force estimation,
object pose estimation, and slip detection. In this paper, we build upon the
FingerVision design, improving already existing control algorithms, and, more
importantly, expanding its range of applicability to more challenging tasks by
utilizing raw skin deformation data for control. In contrast to previous
approaches that rely on the average deformation of the whole sensor surface, we
directly employ local deviations of each spherical marker immersed in the
silicone body of the sensor for feedback control and as input to learning
tasks. We show that with such input, substances of varying texture and
viscosity can be distinguished on the basis of tactile sensations evoked while
stirring them. As another application, we learn a mapping between skin
deformation and force applied to an object. To demonstrate the full range of
capabilities of the proposed controllers, we deploy them in a challenging
architectural assembly task that involves inserting a load-bearing element
underneath a bendable plate at the point of maximum load.
