30 0

Full metadata record

DC FieldValueLanguage
dc.contributor.author서일홍-
dc.date.accessioned2019-12-10T07:44:27Z-
dc.date.available2019-12-10T07:44:27Z-
dc.date.issued2018-12-
dc.identifier.citationIEEE ROBOTICS AND AUTOMATION LETTERS, v. 4, no. 2, page. 293-300en_US
dc.identifier.issn2377-3766-
dc.identifier.urihttps://ieeexplore.ieee.org/document/8585125-
dc.identifier.urihttp://repository.hanyang.ac.kr/handle/20.500.11754/120959-
dc.description.abstractWe propose a method to generate an order for learning and transferring motor skills based on motion complexity, then evaluate the order to learn motor skills of a task and transfer them to another task as a form of reinforcement learning (RL). Here, motion complexity refers to the complexity calculated from multiple motion trajectories of a task. To do this, multiple human demonstrations are extracted and clustered to calculate motion complexity and identify the motor skills involved in a task. The motion trajectories of the task are then used to calculate the motion complexity considering temporal entropy and spatial entropy. Finally, both orders [Simple-to-Complex] and [Complex-to-Simple] are generated to learn and transfer motor skills based on the motion complexities of multiple tasks. To evaluate these orders, two tasks [Drawing] and [Fitting] are performed using an actual robotic arm. To verify the learning and transfer processes, we apply our method to three different figures as well as to pegs and holes of three different shapes and analyze the experimental results. In addition, we provide guidelines for using the [Simple-to-Complex] and [Complex-to-Simple] orders in RL.en_US
dc.description.sponsorshipThis work was supported in part by the Technology Innovation Industrial Program funded by the Ministry of Trade (MI, South Korea) [10048320&10073161] and in part byMSIT under the Institute for Information and Communications Technology Promotion (IITP) Grant 2018-0-00622.en_US
dc.language.isoen_USen_US
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCen_US
dc.subjectMotor skill transferen_US
dc.subjectorderingen_US
dc.subjectmotion complexityen_US
dc.subjectreinforcement learningen_US
dc.subjectrobot manipulationen_US
dc.titleRelationship between the Order for Motor Skill Transfer and Motion Complexity in Reinforcement Learningen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/LRA.2018.2889026-
dc.relation.page1-8-
dc.relation.journalIEEE Robotics and Automation Letters-
dc.contributor.googleauthorCho, Nam Jun-
dc.contributor.googleauthorLee, Sang Hyoung-
dc.contributor.googleauthorSuh, Il Hong-
dc.contributor.googleauthorKim, Hong-Seok-
dc.relation.code2018029112-
dc.sector.campusS-
dc.sector.daehakCOLLEGE OF ENGINEERING[S]-
dc.sector.departmentDEPARTMENT OF ELECTRONIC ENGINEERING-
dc.identifier.pidihsuh-
dc.identifier.orcidhttps://orcid.org/0000-0002-0981-329X-
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > ELECTRONIC ENGINEERING(융합전자공학부) > Articles
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE