Uploaded May 2018 | Updated September 2026, 3 weeks ago
ICRA 2018 Spotlight Video
Interactive Session Wed AM Pod D.1
Authors: Han, Seunghyun; Kim, Taekyoung; Kim, Dooyoung; Park, Yong-Lae; Jo, Sungho
Title: Use of Deep Learning for Characterization of Microfluidic Soft Sensors
Abstract:
Soft sensors made of highly deformable materials are one of the enabling technologies to various soft robotic systems. However, major drawbacks of soft sensors compared with traditional sensors are their nonlinearity and hysteresis in response, which are prominent especially in microfluidic soft sensors. In this research, we propose to address the above issues of soft sensors by taking advantage of deep learning. We implemented a hierarchical recurrent sensing network, a type of recurrent neural network model, to the calibration of soft sensors for estimating both magnitude and location of a contact pressure simultaneously. The proposed approach in this paper were not only able to model the nonlinear characteristic with hysteresis of the pressure response, but also find the location of the pressure.
ICRA 2018 Spotlight Video
Interactive Session Wed AM Pod D.1
Authors: Han, Seunghyun; Kim, Taekyoung; Kim, Dooyoung; Park, Yong-Lae; Jo, Sungho
Title: Use of Deep Learning for Characterization of Microfluidic Soft Sensors
Abstract:
Soft sensors made of highly deformable materials are one of the enabling technologies to various soft robotic systems. However, major drawbacks of soft sensors compared with traditional sensors are their nonlinearity and hysteresis in response, which are prominent especially in microfluidic soft sensors. In this research, we propose to address the above issues of soft sensors by taking advantage of deep learning. We implemented a hierarchical recurrent sensing network, a type of recurrent neural network model, to the calibration of soft sensors for estimating both magnitude and location of a contact pressure simultaneously. The proposed approach in this paper were not only able to model the nonlinear characteristic with hysteresis of the pressure response, but also find the location of the pressure.
