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dc.contributor.author Lee, Deokjin -
dc.contributor.author Choi, Kiyoung -
dc.contributor.author Kim, Junyoung -
dc.contributor.author Yun, Wonbum -
dc.contributor.author Kim, Taehoon -
dc.contributor.author Nam, Kanghyun -
dc.contributor.author Oh, Sehoon -
dc.date.accessioned 2024-02-09T03:10:16Z -
dc.date.available 2024-02-09T03:10:16Z -
dc.date.created 2023-09-15 -
dc.date.issued 2023-06-28 -
dc.identifier.isbn 9781665476331 -
dc.identifier.issn 2159-6255 -
dc.identifier.uri http://hdl.handle.net/20.500.11750/47922 -
dc.description.abstract The trend in robotics has shifted from collaboration with humans to learning and reproducing human skills, reflecting a growing social demand. In response, it is imperative to consider both hardware and software aspects in the design of robots. On the hardware side, the robot should be equipped with adequate sensors for mimicking human motion and force, and its design should meet necessary requirements such as workspace, degree of freedom, payload capacity, and weight, all of which are contingent upon the intended use of the robot. On the software side, the robot should be equipped with a real-time system and stable control algorithms to ensure safe operation. This paper presents the ExSLeR arm which meets the requirements for human skill learning. The performance of the ExSLeR arm is validated by a set of experiments through motion tracking with heavy payload and compliant interaction control tasks. © 2023 IEEE. -
dc.language English -
dc.publisher IEEE Robotics and Automation Society (RAS), IEEE Industrial Electronics Society (IES), ASME Dynamic Systems and Control Division (DSCD) -
dc.relation.ispartof 2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) -
dc.title ExSLeR: Development of a Robotic Arm for Human Skill Learning -
dc.type Conference Paper -
dc.identifier.doi 10.1109/AIM46323.2023.10196166 -
dc.identifier.wosid 001051263900027 -
dc.identifier.scopusid 2-s2.0-85168418958 -
dc.identifier.bibliographicCitation IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2023, pp.209 - 214 -
dc.identifier.url https://aim2023.org/assets/aim2023-program-book-formated_Final.pdf -
dc.citation.conferenceDate 2023-06-28 -
dc.citation.conferencePlace US -
dc.citation.conferencePlace Seattle -
dc.citation.endPage 214 -
dc.citation.startPage 209 -
dc.citation.title IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2023 -
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남강현
Nam, Kanghyun남강현

Department of Robotics and Mechatronics Engineering

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