 
|
|
|
チョウ ゼンシュン
Zhang Shanjun 張 善俊 所属 神奈川大学 情報学部 計算機科学科 神奈川大学大学院 理学研究科 理学専攻（情報科学領域） 職種 教授 |
|
| 言語種別 | 英語 |
| 発行・発表の年月 | 2022/08 |
| 形態種別 | その他論文 |
| 査読 | 査読あり |
| 標題 | Robotic grasping target detection based on domain randomization |
| 執筆形態 | 共著 |
| 掲載誌名 | 2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering (ARACE) |
| 掲載区分 | 国外 |
| 出版社・発行元 | IEEE |
| 巻・号・頁 | pp.171-176 |
| 著者・共著者 | Jiyuan Liu,Junqi Luo,Zhenyu Zhang,Daopeng Liu,Shanjun Zhang,Liucun
Zhu |
| 概要 | Deep learning has been a great
success in robotic vision grasping, which is largely due to its adaptive learning capability and large-scale training samples. However, the hand-crafted datasets may suffer the dilemma of time-cost and quality. In this paper, a robot grasping target detection algorithm based on synthetic data is proposed. The training samples are generated quickly and accurately by domain randomization technique. |
| researchmap用URL | https://doi.org/10.1109/ARACE56528.2022.00038 |
Copyright © Kanagawa University. All Rights Reserved.