Abstract
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 language | English |
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Title of host publication | 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 8957-8962 |
Number of pages | 6 |
ISBN (Electronic) | 978-1-7281-6212-6 |
ISBN (Print) | 978-1-7281-6213-3 |
DOIs | |
Publication status | Published - 10 Feb 2021 |
Event | International Conference on Intelligent Robots and Systems (IROS) - Various venues Duration: 24 Oct 2020 → 24 Jan 2021 https://www.ieee-ras.org/conferences-workshops/financially-co-sponsored/iros |
Publication series
Name | IEEE International Conference on Intelligent Robots and Systems |
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ISSN (Print) | 2153-0858 |
ISSN (Electronic) | 2153-0866 |
Conference
Conference | International Conference on Intelligent Robots and Systems (IROS) |
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Period | 24/10/20 → 24/01/21 |
Internet address |
Keywords
- Charge coupled devices
- Recurrent neural networks
- Time series analysis
- Tactile sensors
- Robot sensing systems
- Cameras
- Strain