Naslov
Convolutional autoencoder for feature extraction in tactile sensing

Autori
Polic, Marsela ; Krajacic, Ivona ; Lepora, Nathan ; Orsag, Matko

Izvornik
IEEE Robotics and Automation Letters (2377-3766) 4 (2019), 4; 3671-3678

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Feature extraction , Pins , Tactile sensors , Training
(Taktilni senzori, Neuronske mreže, Značajke)

Sažetak
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 shallow neural 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.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika

POVEZANOST RADA