A Survey of Robotic Grippers Based on Task-Based Hand Motions and Mechanical Dexterity

Abstract

Extensive research has been conducted on grippers and robotic hands to enhance motion tasks, ranging from simple two-finger mechanisms to intricate designs resembling human-hand configurations. Furthermore, the importance of gripper capabilities has grown significantly with the advancement of learning-based algorithms and vision technologies. In particular, designs inspired by human hands and fingers have increased in complexity and degrees of freedom (DOF), often incorporating linkage-based mechanisms, soft materials, and other innovative approaches. However, the wide diversity of gripper types and robotic hands presents challenges in establishing a unified standard for classification and evaluation. This variability complicates the accurate assessment of gripper capabilities, making the evaluation process difficult and often ambiguous. In this study, a large body of research on robotic hands and grippers was analyzed and categorized. These studies were evaluated based on factors such as degrees of actuation (DOA), number of fingers, and other relevant parameters. To ensure consistency in the analytical and classification processes, specific evaluation criteria derived from previous works were established. The primary objective of this study is to analyze and classify task-based hand motions and gripper designs, and to quantitatively assess them using defined indices. Based on these indices and analyses, we aim to evaluate how complex the task-based hand motions the grippers can perform.