Human-like dexterous manipulation for anthropomorphic five-fingered hands: A review

Abstract

Humans excel at dexterous manipulation; however, achieving human-level dexterity remains a significant challenge for robots. Technological breakthroughs in the design of anthropomorphic robotic hands, as well as advancements in visual and tactile perception, have demonstrated significant advantages in addressing this issue. However, coping with the inevitable uncertainty caused by unstructured and dynamic environments in human-like dexterous manipulation tasks, especially for anthropomorphic five-fingered hands, remains an open problem. In this paper, we present a focused review of human-like dexterous manipulation for anthropomorphic five-fingered hands. We begin by defining human-like dexterity and outlining the tasks associated with human-like robot dexterous manipulation. Subsequently, we delve into anthropomorphism and anthropomorphic five-fingered hands, covering definitions, robotic design, and evaluation criteria. Furthermore, we review the learning methods for achieving human-like dexterity in anthropomorphic five-fingered hands, including imitation learning, reinforcement learning and their integration. Finally, we discuss the existing challenges and propose future research directions. This review aims to stimulate interest in scientific research and future applications.