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Deep Learning of Structure-Borne Sound for Contact Classification in Robotics

Neumann, Michael (2017) Deep Learning of Structure-Borne Sound for Contact Classification in Robotics. DLR-Interner Bericht. DLR-IB-RM-OP-2017-100. Master's. TU Ilmenau.

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A complete perception is necessary for robots in unstructured surroundings. This is especially important for manipulation tasks in which the robot is in contact with the environment. Since sensing modalities have advantages and disadvantages in different situations, sensory information from multiple sources may help achieve a more complete and accurate perception. The possibility of using structure-born sound for the purpose of gaining information about a contact is introduced in this work. A method for material classification is developed and demonstrated. Additionally, the possibility to estimate the position of the contact is analyzed. In order to avoid the difficulty of modeling the behavior of structure-borne sound, a machine learning algorithm is applied. The algorithm used is the DLR-VAE, which is an improved version of the variational autoencoder. Various parameters, which influence the accuracy of the classification are identified and analyzed. Furthermore, the resulting model is tested on the real robot system.

Item URL in elib:https://elib.dlr.de/113389/
Document Type:Monograph (DLR-Interner Bericht, Master's)
Title:Deep Learning of Structure-Borne Sound for Contact Classification in Robotics
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Refereed publication:No
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Keywords:Deep Learning, Variational Auto Encoder, Structure-Borne Sound, Learning, Classification, Contact Analysis, Robotic Manipulation
Institution:TU Ilmenau
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 - Vorhaben Intelligente Mobilität (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013) > Cognitive Robotics
Deposited By: Nottensteiner, Korbinian
Deposited On:24 Jul 2017 11:43
Last Modified:18 Jul 2023 11:49

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