Title | Visual-tactile Sensing for Real-time Liquid Volume Estimation in Grasping |
Author | |
DOI | |
Publication Years | 2022
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Conference Name | IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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ISSN | 2153-0858
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ISBN | 978-1-6654-7928-8
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Source Title | |
Pages | 12542-12549
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Conference Date | 23-27 Oct. 2022
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Conference Place | Kyoto, Japan
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Publication Place | 345 E 47TH ST, NEW YORK, NY 10017 USA
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Publisher | |
Abstract | We propose a deep visuo-tactile model for real-time estimation of the liquid inside a deformable container in a proprioceptive way. We fuse two sensory modalities, i.e., the raw visual inputs from the RGB camera and the tactile cues from our specific tactile sensor without any extra sensor calibrations. The robotic system is well controlled and adjusted based on the estimation model in real time. The main contributions and novelties of our work are listed as follows: 1) Explore a proprioceptive way for liquid volume estimation by developing an end-to-end predictive model with multi-modal convolutional networks, which achieve a high precision with an error of similar to 2 ml in the experimental validation. 2) Propose a multi-task learning architecture which comprehensively considers the losses from both classification and regression tasks, and comparatively evaluate the performance of each variant on the collected data and actual robotic platform. 3) Utilize the proprioceptive robotic system to accurately serve and control the requested volume of liquid, which is continuously flowing into a deformable container in real time. 4) Adaptively adjust the grasping plan to achieve more stable grasping and manipulation according to the real-time liquid volume prediction. |
Keywords | |
SUSTech Authorship | Others
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Language | English
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URL | [Source Record] |
Indexed By | |
WOS Research Area | Automation & Control Systems
; Computer Science
; Engineering
; Robotics
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WOS Subject | Automation & Control Systems
; Computer Science, Artificial Intelligence
; Engineering, Electrical & Electronic
; Robotics
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WOS Accession No | WOS:000909405303133
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Data Source | IEEE
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PDF url | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9981153 |
Citation statistics |
Cited Times [WOS]:0
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Document Type | Conference paper |
Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/420617 |
Department | Department of Mechanical and Energy Engineering |
Affiliation | 1.Department of Computer Science, The University of Hong Kong 2.Department of Biomedical Engineering, City University of Hong Kong 3.Department of Mechanical and Energy Engineering, Southern University of Science and Technology |
Recommended Citation GB/T 7714 |
Fan Zhu,Ruixing Jia,Lei Yang,et al. Visual-tactile Sensing for Real-time Liquid Volume Estimation in Grasping[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2022:12542-12549.
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