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Article Dans Une Revue IEEE Transactions on Industrial Informatics Année : 2020

A Deep Learning Framework for Tactile Recognition of Known as well as Novel Objects

Résumé

This paper addresses the recognition of daily-life objects by a robot equipped with tactile sensors. The main contribution is a deep learning framework that can recognize objects already touched as well as objects never touched before. To this end, we train a Deconvolutional Neural Network that generates synthetic tactile data for novel classes. Then, we use both these synthetic data and the real data collected by touching objects, to train a Convolutional Neural Network to recognize both known (trained) objects and novel objects. Furthermore, we propose a method for integrating newly encountered data into novel classes. Finally, we evaluate the framework using the largest available dataset of tactile objects descriptions.
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Dates et versions

hal-02012628 , version 1 (08-02-2019)

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Zineb Abderrahmane, Gowrishankar Ganesh, André Crosnier, Andrea Cherubini. A Deep Learning Framework for Tactile Recognition of Known as well as Novel Objects. IEEE Transactions on Industrial Informatics, 2020, 16 (1), pp.423-432. ⟨10.1109/TII.2019.2898264⟩. ⟨hal-02012628⟩
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