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

TacFR-Gripper: A Reconfigurable Fin Ray-Based Compliant Robotic Gripper with Tactile Skin for In-Hand Manipulation