TacFR-Gripper：A Reconfigurable Soft Robotic Gripper with Tactile Skin for In-Hand Manipulation

Abstract

This paper introduces the TacFR-Gripper, a novel reconfigurable soft robotic gripper inspired by the Fin Ray effect and equipped with tactile skin. The gripper incorporates a four-bar mechanism for accurate finger bending and a reconfigurable design to change the relative positions between the fingers and palm, which can enable precise and adaptable object grasping. This 5 Degree-of-Freedom (DoF) soft gripper can facilitate dexterous manipulation of objects with diverse shapes and stiffness and is beneficial to the safe and efficient grasping of delicate objects. An array of Force Sensitive Resistor (FSR) sensors is embedded within each robotic fingertip to serve as the tactile skin, enabling the robot to perceive contact information during manipulation. Moreover, we implemented a Graph Neural Network (GNN)-based tactile perception approach to enable reliable grasping without accidental slip or excessive force. To verify the effectiveness of the gripper, we provide detailed workspace analysis to evaluate the grasping performance of the gripper and conducted three experiments, including 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.