Shape reconstruction of CCD camera-based soft tactile sensors

Gabor Soter, Helmut Hauser, Andrew T Conn, Jonathan M Rossiter*, Kohei Nakajima

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

7 Citations (Scopus)
118 Downloads (Pure)


CCD camera-based tactile sensors provide high-resolution information about the deformation of soft and elastic interfaces. However, they have poor scalibility as it is difficult to sense a large surface area without increasing the distance between the camera and the interface or using multiple processing chips. For example, using such tactile sensors for a whole robotic arm is not yet possible. In this work, we demonstrate a data driven method that can reconstruct the high-resolution information about deformation of the soft interface while keeping the space requirements and power consumption relatively low. Our modified tactile sensor incorporates two independent sensing techniques, one low- and one high-resolution, and we learn to map to the latter from the former. As a low-resolution sensor, we use liquid-filled channels that transmit the information from the location of the tactile interaction to a rigid display, where the liquid displacements are tracked by a CCD camera. Simultaneously, the same interaction is measured by tracking the markers on the bottom of the sensor using a second CCD camera. After data collection, we train two different machine learning models to reconstruct the time series of the high-resolution sensor. By training a convolutional autoencoder (CAE) and attaching it to the recurrent neural network (RNN), we demonstrate the reconstruction of high-resolution video frames using only the time series of the low-resolution sensor.
Original languageEnglish
Title of host publication2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Electronic)978-1-7281-6212-6
ISBN (Print)978-1-7281-6213-3
Publication statusPublished - 10 Feb 2021
EventInternational Conference on Intelligent Robots and Systems (IROS) - Various venues
Duration: 24 Oct 202024 Jan 2021

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866


ConferenceInternational Conference on Intelligent Robots and Systems (IROS)
Internet address


  • Charge coupled devices
  • Recurrent neural networks
  • Time series analysis
  • Tactile sensors
  • Robot sensing systems
  • Cameras
  • Strain


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