Full metadata record
DC Field | Value | Language |
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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.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.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.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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