Multilayer perceptrons for bio-inspired friction estimation
Few years old children lift and manipulate unfamiliar objects more dexterously than today's robots. Therefore, it has arisen an interest at the artificial intelligence community to look for inspiration on neurophysiological studies to design better models for the robots. The estimation of the f...
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| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_03029743_v5097LNAI_n_p828_MatukHerrera |
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todo:paper_03029743_v5097LNAI_n_p828_MatukHerrera2023-10-03T15:19:04Z Multilayer perceptrons for bio-inspired friction estimation Matuk Herrera, R. Artificial intelligence Bionics Compression ratio (machinery) Cybernetics Electric loads Estimation Finite element method Machine design Multilayers Neural networks Pattern recognition systems Robotics Soft computing Tribology Bio-inspired Compression ratios Dexterous manipulation Finite element analysis Friction co-efficient Friction estimation Hidden neurons Input patterns Intelligence communities International conferences Mechanoreceptors Multi-layer perceptrons Performance analyses Friction Few years old children lift and manipulate unfamiliar objects more dexterously than today's robots. Therefore, it has arisen an interest at the artificial intelligence community to look for inspiration on neurophysiological studies to design better models for the robots. The estimation of the friction coefficient of the object's material is a crucial information in a human dexterous manipulation. Humans estimate the friction coefficient based on the responses of their tactile mechanoreceptors. In this paper, finite element analysis was used to model a finger and an object. Simulated human afferent responses were then obtained for different friction coefficients. Multiple multilayer perceptrons that received as input simulated human afferent responses, and gave as output an estimation of the friction coefficient, were trained and tested. A performance analysis was carried out to verify the influence of the following factors: number of hidden neurons, compression ratio of the input pattern, partitions of the input pattern. © 2008 Springer-Verlag Berlin Heidelberg. Fil:Matuk Herrera, R. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. SER info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_03029743_v5097LNAI_n_p828_MatukHerrera |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Artificial intelligence Bionics Compression ratio (machinery) Cybernetics Electric loads Estimation Finite element method Machine design Multilayers Neural networks Pattern recognition systems Robotics Soft computing Tribology Bio-inspired Compression ratios Dexterous manipulation Finite element analysis Friction co-efficient Friction estimation Hidden neurons Input patterns Intelligence communities International conferences Mechanoreceptors Multi-layer perceptrons Performance analyses Friction |
| spellingShingle |
Artificial intelligence Bionics Compression ratio (machinery) Cybernetics Electric loads Estimation Finite element method Machine design Multilayers Neural networks Pattern recognition systems Robotics Soft computing Tribology Bio-inspired Compression ratios Dexterous manipulation Finite element analysis Friction co-efficient Friction estimation Hidden neurons Input patterns Intelligence communities International conferences Mechanoreceptors Multi-layer perceptrons Performance analyses Friction Matuk Herrera, R. Multilayer perceptrons for bio-inspired friction estimation |
| topic_facet |
Artificial intelligence Bionics Compression ratio (machinery) Cybernetics Electric loads Estimation Finite element method Machine design Multilayers Neural networks Pattern recognition systems Robotics Soft computing Tribology Bio-inspired Compression ratios Dexterous manipulation Finite element analysis Friction co-efficient Friction estimation Hidden neurons Input patterns Intelligence communities International conferences Mechanoreceptors Multi-layer perceptrons Performance analyses Friction |
| description |
Few years old children lift and manipulate unfamiliar objects more dexterously than today's robots. Therefore, it has arisen an interest at the artificial intelligence community to look for inspiration on neurophysiological studies to design better models for the robots. The estimation of the friction coefficient of the object's material is a crucial information in a human dexterous manipulation. Humans estimate the friction coefficient based on the responses of their tactile mechanoreceptors. In this paper, finite element analysis was used to model a finger and an object. Simulated human afferent responses were then obtained for different friction coefficients. Multiple multilayer perceptrons that received as input simulated human afferent responses, and gave as output an estimation of the friction coefficient, were trained and tested. A performance analysis was carried out to verify the influence of the following factors: number of hidden neurons, compression ratio of the input pattern, partitions of the input pattern. © 2008 Springer-Verlag Berlin Heidelberg. |
| format |
SER |
| author |
Matuk Herrera, R. |
| author_facet |
Matuk Herrera, R. |
| author_sort |
Matuk Herrera, R. |
| title |
Multilayer perceptrons for bio-inspired friction estimation |
| title_short |
Multilayer perceptrons for bio-inspired friction estimation |
| title_full |
Multilayer perceptrons for bio-inspired friction estimation |
| title_fullStr |
Multilayer perceptrons for bio-inspired friction estimation |
| title_full_unstemmed |
Multilayer perceptrons for bio-inspired friction estimation |
| title_sort |
multilayer perceptrons for bio-inspired friction estimation |
| url |
http://hdl.handle.net/20.500.12110/paper_03029743_v5097LNAI_n_p828_MatukHerrera |
| work_keys_str_mv |
AT matukherrerar multilayerperceptronsforbioinspiredfrictionestimation |
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1782029690997833728 |