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• W4386883438 abstract "The task of liquid volume estimation based on tactile sensing is challenging. Previous researches mainly focused on the classification of different contents in the container, rather than achieving accurate estimation of a certain property of the contents in container, such as volume or viscosity. In addition, few studies are conducted for the regression task with advanced tactile sensors, but the performance is unsatisfactory. We propose a new deep-learning algorithm for processing the tactile signals collected from the photoresistors in the task of liquid content estimation. The proposed algorithm doesn’t need hand-crafted features. In addition, we use attention after BLSTM, so that more important features can be selected to achieve better performance for liquid volume estimation. We utilize a bi-directional Long Short-Term Memory (BLSTM) architecture, which allows for the incorporation of information from both preceding and subsequent time, thereby providing a more comprehensive and informative feature set for learning. Additionally, we employ an attention mechanism following the BLSTM, allowing for the selection of more critical features and improved performance in liquid volume estimation. To prove the effectiveness of our method, we conduct experiments utilizing tactile sensors on a physical robot, achieving superior performance in liquid volume estimation." @default.
• W4386883438 created "2023-09-21" @default.
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• W4386883438 date "2023-07-17" @default.
• W4386883438 modified "2023-10-11" @default.
• W4386883438 title "Attention-enhanced BLSTM Network for Liquid Volume Estimation based on Tactile Sensing" @default.
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• W4386883438 doi "https://doi.org/10.1109/rcar58764.2023.10250000" @default.
• W4386883438 hasPublicationYear "2023" @default.
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