Irregular Depth Tiles: Automatically Generated Data Used for Network-based Robotic Grasping in 2D Dense Clutter
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Da-Wit | - |
dc.contributor.author | Jo, HyunJun | - |
dc.contributor.author | Song, Jae-Bok | - |
dc.date.accessioned | 2022-02-17T14:41:16Z | - |
dc.date.available | 2022-02-17T14:41:16Z | - |
dc.date.created | 2022-02-09 | - |
dc.date.issued | 2021-10 | - |
dc.identifier.issn | 1598-6446 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/136114 | - |
dc.description.abstract | Recent advances in deep learning have enabled robots to grasp objects even in complex environments. However, a large amount of data is required to train the deep-learning network, which leads to a high cost in acquiring the learning data owing to the use of an actual robot or simulator. This paper presents a new form of grasp data that can be generated automatically to minimize the data-collection cost. The depth image is converted into simplified grasp data called an irregular depth tile that can be used to estimate the optimal grasp pose. Additionally, we propose a new grasping algorithm that employs different methods according to the amount of free space in the bounding box of the target object. This algorithm exhibited a significantly higher success rate than the existing grasping methods in grasping experiments in complex environments. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | INST CONTROL ROBOTICS & SYSTEMS, KOREAN INST ELECTRICAL ENGINEERS | - |
dc.title | Irregular Depth Tiles: Automatically Generated Data Used for Network-based Robotic Grasping in 2D Dense Clutter | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Song, Jae-Bok | - |
dc.identifier.doi | 10.1007/s12555-019-0758-1 | - |
dc.identifier.scopusid | 2-s2.0-85111364564 | - |
dc.identifier.wosid | 000677961200015 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, v.19, no.10, pp.3428 - 3434 | - |
dc.relation.isPartOf | INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS | - |
dc.citation.title | INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS | - |
dc.citation.volume | 19 | - |
dc.citation.number | 10 | - |
dc.citation.startPage | 3428 | - |
dc.citation.endPage | 3434 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.identifier.kciid | ART002763607 | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | Automation & Control Systems | - |
dc.relation.journalWebOfScienceCategory | Automation & Control Systems | - |
dc.subject.keywordAuthor | Data generation | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | grasping | - |
dc.subject.keywordAuthor | manipulation | - |
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