  Over the past few decades, efforts have been made towards robust robotic
grasping, and therefore dexterous manipulation. The soft gripper has shown
their potential in robust grasping due to their inherent properties-low,
control complexity, and high adaptability. However, the deformation of the soft
gripper when interacting with objects bring inaccuracy of grasped objects,
which causes instability for robust grasping and further manipulation. In this
paper, we present an omni-directional adaptive soft finger that can sense
deformation based on embedded optical fibers and the application of machine
learning methods to interpret transmitted light intensities. Furthermore, to
use tactile information provided by a soft finger, we design a low-cost and
multi degrees of freedom gripper to conform to the shape of objects actively
and optimize grasping policy, which is called Rigid-Soft Interactive Grasping.
Two main advantages of this grasping policy are provided: one is that a more
robust grasping could be achieved through an active adaptation; the other is
that the tactile information collected could be helpful for further
manipulation.
