Deep Neural Network Based Electrical Impedance Tomographic Sensing Methodology for Large-Area Robotic Tactile Sensing

Electrical impedance tomography (EIT) based tactile sensor offers significant benefits on practical deployment because of its sparse electrode allocation, including durability, large-area scalability, and low fabrication cost, but the degradation of a tactile spatial resolution has remained challeng...

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Published in:	IEEE transactions on robotics Vol. 37; no. 5; pp. 1570 - 1583
Main Authors:	Park, Hyunkyu, Park, Kyungseo, Mo, Sangwoo, Kim, Jung
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.10.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Artificial intelligence (AI) based methods Artificial neural networks Computational efficiency Conductivity deep learning in robotics and automation Discrimination Electrical impedance Electrodes force and tactile sensing Hardness tests Image reconstruction Indentation Neural networks Reconstruction Robot sensing systems Sensitivity Sensors Spatial resolution Tactile sensors (robotics) Tomography Touch Voltage measurement
ISSN:	1552-3098, 1941-0468
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Abstract	Electrical impedance tomography (EIT) based tactile sensor offers significant benefits on practical deployment because of its sparse electrode allocation, including durability, large-area scalability, and low fabrication cost, but the degradation of a tactile spatial resolution has remained challenging. This article describes a deep neural network based EIT reconstruction framework, the EIT neural network (EIT-NN), alleviating this tradeoff between tactile sensing performance and hardware simplicity. EIT-NN learns a computationally efficient, nonlinear reconstruction attribute, achieving high-resolution tactile sensation and well-generalized reconstruction capability to address arbitrary complex touch modalities. We train EIT-NN by presenting a sim-to-real dataset synthesis strategy for computationally efficient generalizability. Furthermore, we propose a spatial sensitivity aware mean-squared error loss function, which uses an intrinsic spatial sensitivity of the sensor to guarantee a well-posed EIT operation. We validate an outperformance of EIT-NN against conventional EIT sensing methods by conducting a simulation study, a single-touch indentation test, and a two-point discrimination test. The results show improved spatial resolution, sensitivity, and localization accuracy. The beneficial features of the generalized sensing of EIT-NN were demonstrated by examining touch modality discrimination performance.
AbstractList	Electrical impedance tomography (EIT) based tactile sensor offers significant benefits on practical deployment because of its sparse electrode allocation, including durability, large-area scalability, and low fabrication cost, but the degradation of a tactile spatial resolution has remained challenging. This article describes a deep neural network based EIT reconstruction framework, the EIT neural network (EIT-NN), alleviating this tradeoff between tactile sensing performance and hardware simplicity. EIT-NN learns a computationally efficient, nonlinear reconstruction attribute, achieving high-resolution tactile sensation and well-generalized reconstruction capability to address arbitrary complex touch modalities. We train EIT-NN by presenting a sim-to-real dataset synthesis strategy for computationally efficient generalizability. Furthermore, we propose a spatial sensitivity aware mean-squared error loss function, which uses an intrinsic spatial sensitivity of the sensor to guarantee a well-posed EIT operation. We validate an outperformance of EIT-NN against conventional EIT sensing methods by conducting a simulation study, a single-touch indentation test, and a two-point discrimination test. The results show improved spatial resolution, sensitivity, and localization accuracy. The beneficial features of the generalized sensing of EIT-NN were demonstrated by examining touch modality discrimination performance.
Author	Park, Kyungseo Park, Hyunkyu Kim, Jung Mo, Sangwoo
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SubjectTerms	Artificial intelligence (AI) based methods Artificial neural networks Computational efficiency Conductivity deep learning in robotics and automation Discrimination Electrical impedance Electrodes force and tactile sensing Hardness tests Image reconstruction Indentation Neural networks Reconstruction Robot sensing systems Sensitivity Sensors Spatial resolution Tactile sensors (robotics) Tomography Touch Voltage measurement
Title	Deep Neural Network Based Electrical Impedance Tomographic Sensing Methodology for Large-Area Robotic Tactile Sensing
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