A robot grasping detection network based on flexible selection of multi-modal feature fusion structure-Reference-Cited by-同舟云学术

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A robot grasping detection network based on flexible selection of multi-modal feature fusion structure

Published:2024-03 Issue:6 Volume:54 Page:5044-5061
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Wang Yuhan,Guo Zhibo,Chen Yu,Guo Chaiqi,Xia Meizhen,Qi Tingyue

Funder

Jiangsu Agricultural Science and Technology Innovation Fund

Teaching Reform Research Project of Yangzhou University

Medical Innovation Transformation Special Fund of Yangzhou University-New Medical Interdisciplinary Innovation Team

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10489-024-05427-9.pdf

Reference57 articles.

1. Hu Y, Wu X, Geng P, Li Z (2019) Evolution strategies learning with variable impedance control for grasping under uncertainty. IEEE Trans Industr Electron 66(10):7788–7799

2. Li G, Li N, Chang F, Liu C (2022) Adaptive graph convolutional network with adversarial learning for skeleton-based action prediction. IEEE Transactions on Cognitive and Developmental Systems 14(3):1258–1269

3. Buzzatto J, Chapman J, Shahmohammadi M, Sanches F, Nejati M, Matsunaga S, Haraguchi R, Mariyama T, MacDonald B, Liarokapis M (2022) On robotic manipulation of flexible flat cables: Employing a multi-modal gripper with dexterous tips, active nails, and a reconfigurable suction cup module. 2022 IEEE/RSJ International conference on intelligent robots and systems (IROS), pp 1602–1608

4. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. nature 521(7553):436–444

5. Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84–90

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