Difference between revisions of "Conference papers"
From LRDE
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| − | {{Publication |
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| − | | published = true |
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| − | | date = 2022-07-22 |
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| − | | authors = Zhou Zhao, Zhenyu Lu |
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| − | | title = Multi-purpose Tactile Perception Based on Deep Learning in a New Tendon-driven Optical Tactile Sensor |
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| − | | booktitle = 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems |
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| − | | address = Kyoto, Japan |
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| − | | abstract = In this paper, we create a new tendon-connected multi-functional |
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| − | optical tactile sensor, MechTac, for object perception |
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| − | in field of view (TacTip) and location of touching points |
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| − | in the blind area of vision (TacSide). In a multi-point touch task, |
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| − | the information of the TacSide and the TacTip are |
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| − | overlapped to commonly affect the distribution of papillae pins on the TacTip. |
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| − | Since the effects of TacSide are much less obvious to |
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| − | those affected on the TacTip, a perceiving out-of-view neural network (O2VNet) |
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| − | is created to separate the mixed information with unequal affection. |
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| − | To reduce the dependence of the O2VNet on the grayscale information |
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| − | of the image, we create one new binarized convolutional (BConv) layer |
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| − | in front of the backbone of the O2VNet. The O2VNet can not only achieve |
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| − | real-time temporal sequence prediction (34 ms per image), |
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| − | but also attain the average classification accuracy of 99.06%. |
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| − | The experimental results show that the O2VNet can hold high classification accuracy |
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| − | even facing the image contrast changes. |
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| − | | lrdeprojects = Olena |
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| − | | lrdekeywords = Image |
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| − | | lrdenewsdate = 2022-07-22 |
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| − | | note = accepted |
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| − | | type = inproceedings |
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| − | | id = zhao.22.iros |
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| − | | bibtex = |
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| − | @InProceedings<nowiki>{</nowiki> zhao.22.iros, |
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| − | author = <nowiki>{</nowiki>Zhou Zhao and Zhenyu Lu<nowiki>}</nowiki>, |
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| − | title = <nowiki>{</nowiki>Multi-purpose Tactile Perception Based on Deep Learning in a New Tendon-driven Optical Tactile Sensor<nowiki>}</nowiki>, |
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| − | booktitle = <nowiki>{</nowiki>2022 IEEE/RSJ International Conference on Intelligent Robots and Systems<nowiki>}</nowiki>, |
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| − | year = 2022, |
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| − | address = <nowiki>{</nowiki>Kyoto, Japan<nowiki>}</nowiki>, |
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| − | month = october, |
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| − | abstract = <nowiki>{</nowiki>In this paper, we create a new tendon-connected multi-functional |
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| − | optical tactile sensor, MechTac, for object perception |
||
| − | in field of view (TacTip) and location of touching points |
||
| − | in the blind area of vision (TacSide). In a multi-point touch task, |
||
| − | the information of the TacSide and the TacTip are |
||
| − | overlapped to commonly affect the distribution of papillae pins on the TacTip. |
||
| − | Since the effects of TacSide are much less obvious to |
||
| − | those affected on the TacTip, a perceiving out-of-view neural network (O$^2$VNet) |
||
| − | is created to separate the mixed information with unequal affection. |
||
| − | To reduce the dependence of the O$^2$VNet on the grayscale information |
||
| − | of the image, we create one new binarized convolutional (BConv) layer |
||
| − | in front of the backbone of the O$^2$VNet. The O$^2$VNet can not only achieve |
||
| − | real-time temporal sequence prediction (34 ms per image), |
||
| − | but also attain the average classification accuracy of 99.06\%. |
||
| − | The experimental results show that the O$^2$VNet can hold high classification accuracy |
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| − | even facing the image contrast changes. <nowiki>}</nowiki>, |
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| − | note = <nowiki>{</nowiki>accepted<nowiki>}</nowiki> |
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| − | <nowiki>}</nowiki> |
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| − | |||
| − | }} |
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