ExSLeR: Development of a Robotic Arm for Human Skill Learning

Deokjin Lee, Kiyoung Choi, Junyoung Kim, Wonbum Yun, Taehoon Kim, Kanghyun Nam, Sehoon Oh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

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.

Original languageEnglish
Title of host publication2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages209-214
Number of pages6
ISBN (Electronic)9781665476331
DOIs
StatePublished - 2023
Event2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2023 - Seattle, United States
Duration: 28 Jun 202330 Jun 2023

Publication series

NameIEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
Volume2023-June

Conference

Conference2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2023
Country/TerritoryUnited States
CitySeattle
Period28/06/2330/06/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Fingerprint

Dive into the research topics of 'ExSLeR: Development of a Robotic Arm for Human Skill Learning'. Together they form a unique fingerprint.

Cite this