dc.citation.conferencePlace |
FR |
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dc.citation.endPage |
9403 |
- |
dc.citation.startPage |
9397 |
- |
dc.citation.title |
IEEE International Conference on Robotics and Automation |
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dc.contributor.author |
Park, Dongwon |
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dc.contributor.author |
Seo, Yonghyeok |
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dc.contributor.author |
Chun, Se Young |
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dc.date.accessioned |
2024-01-31T23:06:51Z |
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dc.date.available |
2024-01-31T23:06:51Z |
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dc.date.created |
2021-01-11 |
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dc.date.issued |
2020-06-03 |
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dc.description.abstract |
Rotation invariance has been an important topic in computer vision tasks. Ideally, robot grasp detection should be rotation-invariant. However, rotation-invariance in robotic grasp detection has been only recently studied by using rotation anchor box that are often time-consuming and unreliable for multiple objects. In this paper, we propose a rotation ensemble module (REM) for robotic grasp detection using convolutions that rotates network weights. Our proposed REM was able to outperform current state-of-the-art methods by achieving up to 99.2% (image-wise), 98.6% (object-wise) accuracies on the Cornell dataset with real-time computation (50 frames per second). Our proposed method was also able to yield reliable grasps for multiple objects and up to 93.8% success rate for the real-time robotic grasping task with a 4-axis robot arm for small novel objects that was significantly higher than the baseline methods by 11-56%. |
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dc.identifier.bibliographicCitation |
IEEE International Conference on Robotics and Automation, pp.9397 - 9403 |
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dc.identifier.doi |
10.1109/ICRA40945.2020.9197002 |
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dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/78507 |
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dc.language |
영어 |
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dc.publisher |
Institute of Electrical and Electronics Engineers Inc. |
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dc.title |
Real-Time, Highly Accurate Robotic Grasp Detection using Fully Convolutional Neural Network with Rotation Ensemble Module |
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dc.type |
Conference Paper |
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dc.date.conferenceDate |
2020-05-31 |
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