From Pixels to Percepts: Highly Robust Edge Perception and Contour Following Using Deep Learning and an Optical Biomimetic Tactile Sensor

Deep learning has the potential to have same the impact on robot touch as it has had on robot vision. Optical tactile sensors act as a bridge between the subjects by allowing techniques from vision to be applied to touch. In this letter, we apply deep learning to an optical biomimetic tactile sensor...

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Vydáno v:IEEE robotics and automation letters Ročník 4; číslo 2; s. 2101 - 2107
Hlavní autoři: Lepora, Nathan F., Church, Alex, de Kerckhove, Conrad, Hadsell, Raia, Lloyd, John
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.04.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Artificial neural networks
Biomedical optical imaging
Biomimetics
Deep learning
deep learning in robotics and automation
Force and tactile sensing
Machine learning
Machine vision
Optical imaging
Papillae
Pins
Robots
Sensor arrays
Skin
Tactile sensors
Tactile sensors (robotics)
Touch
ISSN:2377-3766, 2377-3766
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Shrnutí:Deep learning has the potential to have same the impact on robot touch as it has had on robot vision. Optical tactile sensors act as a bridge between the subjects by allowing techniques from vision to be applied to touch. In this letter, we apply deep learning to an optical biomimetic tactile sensor, the TacTip, which images an array of papillae (pins) inside its sensing surface analogous to structures within human skin. Our main result is that the application of a deep convolutional neural network can give reliable edge perception, and, thus a robust policy for planning contact points to move around object contours. Robustness is demonstrated over several irregular and compliant objects with both tapping and continuous sliding, using a model trained only by tapping onto a disk. These results relied on using techniques to encourage generalization to tasks beyond which the model was trained. We expect this is a generic problem in practical applications of tactile sensing that deep learning will solve.
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2019.2899192