{"title":"Empower dexterous robotic hand for human-centric smart manufacturing: A perception and skill learning perspective","authors":"Benhua Gao , Junming Fan , Pai Zheng","doi":"10.1016/j.rcim.2024.102909","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"93 ","pages":"Article 102909"},"PeriodicalIF":9.1000,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Robotics and Computer-integrated Manufacturing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0736584524001960","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.