TY - GEN
T1 - Calibrating a Soft ERT-Based Tactile Sensor with a Multiphysics Model and Sim-to-real Transfer Learning
AU - Lee, Hyosang
AU - Park, Hyunkyu
AU - Serhat, Gokhan
AU - Sun, Huanbo
AU - Kuchenbecker, Katherine J.
PY - 2020/5
Y1 - 2020/5
N2 - Tactile sensors based on electrical resistance tomography (ERT) have shown many advantages for implementing a soft and scalable whole-body robotic skin; however, calibration is challenging because pressure reconstruction is an ill-posed inverse problem. This paper introduces a method for calibrating soft ERT-based tactile sensors using sim-to-real transfer learning with a finite element multiphysics model. The model is composed of three simple models that together map contact pressure distributions to voltage measurements. We optimized the model parameters to reduce the gap between the simulation and reality. As a preliminary study, we discretized the sensing points into a 6 by 6 grid and synthesized single- and two-point contact datasets from the multiphysics model. We obtained another single-point dataset using the real sensor with the same contact location and force used in the simulation. Our new deep neural network architecture uses a de-noising network to capture the simulation-to-real gap and a reconstruction network to estimate contact force from voltage measurements. The proposed approach showed 82% hit rate for localization and 0.51 N of force estimation error performance in singlecontact tests and 78.5% hit rate for localization and 5.0 N of force estimation error in two-point contact tests. We believe this new calibration method has the possibility to improve the sensing performance of ERT-based tactile sensors.
AB - Tactile sensors based on electrical resistance tomography (ERT) have shown many advantages for implementing a soft and scalable whole-body robotic skin; however, calibration is challenging because pressure reconstruction is an ill-posed inverse problem. This paper introduces a method for calibrating soft ERT-based tactile sensors using sim-to-real transfer learning with a finite element multiphysics model. The model is composed of three simple models that together map contact pressure distributions to voltage measurements. We optimized the model parameters to reduce the gap between the simulation and reality. As a preliminary study, we discretized the sensing points into a 6 by 6 grid and synthesized single- and two-point contact datasets from the multiphysics model. We obtained another single-point dataset using the real sensor with the same contact location and force used in the simulation. Our new deep neural network architecture uses a de-noising network to capture the simulation-to-real gap and a reconstruction network to estimate contact force from voltage measurements. The proposed approach showed 82% hit rate for localization and 0.51 N of force estimation error performance in singlecontact tests and 78.5% hit rate for localization and 5.0 N of force estimation error in two-point contact tests. We believe this new calibration method has the possibility to improve the sensing performance of ERT-based tactile sensors.
UR - http://www.scopus.com/inward/record.url?scp=85092726862&partnerID=8YFLogxK
U2 - 10.1109/ICRA40945.2020.9196732
DO - 10.1109/ICRA40945.2020.9196732
M3 - Conference contribution
AN - SCOPUS:85092726862
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 1632
EP - 1638
BT - 2020 IEEE International Conference on Robotics and Automation, ICRA 2020
PB - Institute of Electrical and Electronics Engineers
T2 - 2020 IEEE International Conference on Robotics and Automation, ICRA 2020
Y2 - 31 May 2020 through 31 August 2020
ER -