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.

翻译：本文介绍TacFR-Gripper，一种配备触觉皮肤的可重构Fin Ray软体柔性机器人夹爪，可用于灵巧的手内操作任务。该夹爪能自适应抓取不同形状和刚度等级的物体。机器人手指内嵌有力敏电阻(FSR)传感器阵列构成触觉皮肤，使机器人能够在操作过程中感知接触信息。我们提供了夹爪设计的理论分析，包括运动学分析、工作空间分析及有限元分析，以确定夹爪负载与变形之间的关系。此外，我们实现了基于图神经网络(GNN)的触觉感知方法，确保在无意外滑动或过大作用力的情况下实现可靠抓取。通过三项物理实验量化TacFR-Gripper的性能，旨在：(i)评估不同配置下对各种日常物体的抓取成功率；(ii)验证GNN算法下触觉皮肤在抓取中的有效性；(iii)评估夹爪在物体姿态控制方面的的手内操作能力。实验结果表明，TacFR-Gripper能够以高成功率抓取多种复杂形状物体，并实现灵巧的手内操作。同时，触觉皮肤与GNN算法的集成通过引入操作中的触觉反馈增强了抓取稳定性。项目详情请见网站：https://sites.google.com/view/tacfr-gripper/homepage。