Journal
IEEE ROBOTICS AND AUTOMATION LETTERS
Volume 4, Issue 4, Pages 3671-3678Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2019.2927950
Keywords
Force and tactile sensing; deep learning in robotics and automation; soft sensors and actuators; perception for grasping and manipulation
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Funding
- Croatian Science Foundation [Specularia UIP-2017-05-4042]
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A common approach in the field of tactile robotics is the development of a new perception algorithm for each new application of existing hardware solutions. In this letter, we present a method of dimensionality reduction of an optical-based tactile sensor image output using a convolutional neural network encoder structure. Instead of using various complex perception algorithms, and/ or manually choosing task-specific data features, this unsupervised feature extraction method allows simultaneous online deployment of multiple simple perception algorithms on a common set of black-box features. The method is validated on a set of benchmarking use cases. Contact object shape, edge position, orientation, and indentation depth are estimated using shallowneural networks and machine learning models. Furthermore, a contact force estimator is trained, affirming that the extracted features contain sufficient information on both spatial and mechanical characteristics of the manipulated object.
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