  This paper introduces the TacFR-Gripper, a reconfigurable Fin Ray-based soft
and compliant robotic gripper equipped with tactile skin, which can be used for
dexterous in-hand manipulation tasks. This gripper can adaptively grasp objects
of diverse shapes and stiffness levels. An array of Force Sensitive Resistor
(FSR) sensors is embedded within the robotic finger to serve as the tactile
skin, enabling the robot to perceive contact information during manipulation.
We provide theoretical analysis for gripper design, including kinematic
analysis, workspace analysis, and finite element analysis to identify the
relationship between the gripper's load and its deformation. Moreover, we
implemented a Graph Neural Network (GNN)-based tactile perception approach to
enable reliable grasping without accidental slip or excessive force.
  Three physical experiments were conducted to quantify the performance of the
TacFR-Gripper. These experiments aimed to i) assess the grasp success rate
across various everyday objects through different configurations, ii) verify
the effectiveness of tactile skin with the GNN algorithm in grasping, iii)
evaluate the gripper's in-hand manipulation capabilities for object pose
control. The experimental results indicate that the TacFR-Gripper can grasp a
wide range of complex-shaped objects with a high success rate and deliver
dexterous in-hand manipulation. Additionally, the integration of tactile skin
with the GNN algorithm enhances grasp stability by incorporating tactile
feedback during manipulations. For more details of this project, please view
our website: https://sites.google.com/view/tacfr-gripper/homepage.
