Empower dexterous robotic hand for human-centric smart manufacturing: A perception and skill learning perspective

Abstract

Recent rapid developments of dexterous robotic hands have greatly enhanced the manipulative capabilities of robots, enabling them to perform industrial tasks in human-like dexterity. These advancements not only enhance operational efficiency but also liberate human operators from monotonous tasks, allowing them to focus on creative and intellectually demanding. Despite the considerable attention robotic hands have garnered, existing reviews tend to focus on isolated topics, failing to provide a comprehensive perspective of the manufacturing sector. To empower robotic hands in human-centric smart manufacturing, this paper explores the latest research on holistic perception and dexterous skill learning of robotic hands. Specifically, the perceptual challenges in dexterous manipulation concerning different entities are investigated, including human hand perception, object inside-hand and outside-hand perception based on vision or tactility, and hand-object interactions, which help robots accurately understand environmental information. Furthermore, learning-based control methods are discussed, enhancing the execution capabilities of robotic hands through learning from scratch and learning from human demonstrations. Lastly, this paper identifies current challenges and offers several promising directions for future developments.