  This paper presents a novel design of a soft tactile finger with
omni-directional adaptation using multi-channel optical fibers for rigid-soft
interactive grasping. Machine learning methods are used to train a model for
real-time prediction of force, torque, and contact using the tactile data
collected. We further integrated such fingers in a reconfigurable gripper
design with three fingers so that the finger arrangement can be actively
adjusted in real-time based on the tactile data collected during grasping,
achieving the process of rigid-soft interactive grasping. Detailed sensor
calibration and experimental results are also included to further validate the
proposed design for enhanced grasping robustness.
