  Grasping objects requires tight integration between visual and tactile
feedback. However, there is an inherent difference in the scale at which both
these input modalities operate. It is thus necessary to be able to analyze
tactile feedback in isolation in order to gain information about the surface
the end-effector is operating on, such that more fine-grained features may be
extracted from the surroundings. For tactile perception of the robot, inspired
by the concept of the tactile flow in humans, we present the computational
tactile flow to improve the analysis of the tactile feedback in robots using a
Shadow Dexterous Hand.
  In the computational tactile flow model, given a sequence of pressure values
from the tactile sensors, we define a virtual surface for the pressure values
and define the tactile flow as the optical flow of this surface. We provide
case studies that demonstrate how the computational tactile flow maps reveal
information on the direction of motion and 3D structure of the surface, and
feedback regarding the action being performed by the robot.
