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Robust material classification with a tactile skin using deep learning

Baishya, Shiv S. and Bäuml, Berthold (2016) Robust material classification with a tactile skin using deep learning. In: IEEE International Conference on Intelligent Robots and Systems. IROS 2016, 2016-10-09 - 2016-10-14, Daejeon, South Korea. doi: 10.1109/iros.2016.7758088.

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Attaching a flexible tactile skin to an existing robotic system is relatively easy compared to integrating most other tactile sensor designs. In this paper we show that material classification purely based on the spatio-temporal signal of a flexible tactile skin can be robustly performed in a real world setting. We compare different classification algorithms and feature sets, including features adopted and extended from previous works in tactile material classification and that are based on the signal’s Fourier spectrum. Our convolutional deep learning network architecture, which we also present here, is directly fed with the raw 24000 dimensional sensor signal and performs best by a large margin, reaching a classification accuracy of up to 97.3%.

Item URL in elib:https://elib.dlr.de/109881/
Document Type:Conference or Workshop Item (Speech, Poster)
Title:Robust material classification with a tactile skin using deep learning
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Bäuml, BertholdUNSPECIFIEDhttps://orcid.org/0000-0002-4545-4765UNSPECIFIED
Journal or Publication Title:IEEE International Conference on Intelligent Robots and Systems
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Keywords:tactile skin, material classification, deep learning, convolutional neural networks
Event Title:IROS 2016
Event Location:Daejeon, South Korea
Event Type:international Conference
Event Start Date:9 October 2016
Event End Date:14 October 2016
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space System Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Space System Technology
DLR - Research theme (Project):R - Autonomous Learning Robots [SY]
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013)
Deposited By: Baishya, Shiv
Deposited On:20 Dec 2016 23:05
Last Modified:07 Jun 2024 10:42

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