Dynamic and interactive generation of object handling behaviors by a small humanoid robot using a dynamic neural network model

This study presents experiments on the learning of object handling behaviors by a small humanoid robot using a dynamic neural network model, the recurrent neural network with parametric bias (RNNPB). The first experiment showed that after the robot learned different types of ball handling behaviors...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Neural networks 2006-04, Vol.19 (3), p.323-337
Hauptverfasser: Ito, Masato, Noda, Kuniaki, Hoshino, Yukiko, Tani, Jun
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 337
container_issue 3
container_start_page 323
container_title Neural networks
container_volume 19
creator Ito, Masato
Noda, Kuniaki
Hoshino, Yukiko
Tani, Jun
description This study presents experiments on the learning of object handling behaviors by a small humanoid robot using a dynamic neural network model, the recurrent neural network with parametric bias (RNNPB). The first experiment showed that after the robot learned different types of ball handling behaviors using human direct teaching, the robot was able to generate adequate ball handling motor sequences situated to the relative position between the robot's hands and the ball. The same scheme was applied to a block handling learning task where it was shown that the robot can switch among learned different block handling sequences, situated to the ways of interaction by human supporters. Our analysis showed that entrainment of the internal memory structures of the RNNPB through the interactions of the objects and the human supporters are the essential mechanisms for those observed situated behaviors of the robot
doi_str_mv 10.1016/j.neunet.2006.02.007
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_67997256</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0893608006000311</els_id><sourcerecordid>19379719</sourcerecordid><originalsourceid>FETCH-LOGICAL-c516t-dd44aad0e3c130b768ac6332f1246bc68c7856cd1814bab44ca89a431e5822aa3</originalsourceid><addsrcrecordid>eNqFkTuP1DAQgC0E4vYW_gFCbqBL8CNxnAYJHXAgnUQDtTWxJ7deEvuwk0Xb8Nvxale6DpqZKb55aD5CXnFWc8bVu30dcA241IIxVTNRM9Y9IRuuu74SnRZPyYbpXlaKaXZFrnPeswLqRj4nV1wprlupNuTPx2OA2VsKwVEfFkxgF39Aeo-h1IuPgcaRxmGPdqG7Qk0-3NMBd3DwMWU6HCnQPMM00d06Q4je0RSHuNA1n0ig7rKhnJtgKmn5HdNPOkeH0wvybIQp48tL3pIfnz99v_lS3X27_Xrz4a6yLVdL5VzTADiG0nLJhk5psEpKMXLRqMEqbTvdKuu45s0AQ9NY0D00kmOrhQCQW_L2PPchxV8r5sXMPlucJggY12xU1_edaNV_Qd7Lru9K3JLmDNoUc044mofkZ0hHw5k5CTJ7cxZkToIME6YIKm2vL_PXYUb32HQxUoA3FwCyhWlMEKzPj1ynVd9KUbj3Zw7L2w4ek8nWY7DofCqqjIv-35f8BW-4srA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>19379719</pqid></control><display><type>article</type><title>Dynamic and interactive generation of object handling behaviors by a small humanoid robot using a dynamic neural network model</title><source>MEDLINE</source><source>Elsevier ScienceDirect Journals</source><creator>Ito, Masato ; Noda, Kuniaki ; Hoshino, Yukiko ; Tani, Jun</creator><creatorcontrib>Ito, Masato ; Noda, Kuniaki ; Hoshino, Yukiko ; Tani, Jun</creatorcontrib><description>This study presents experiments on the learning of object handling behaviors by a small humanoid robot using a dynamic neural network model, the recurrent neural network with parametric bias (RNNPB). The first experiment showed that after the robot learned different types of ball handling behaviors using human direct teaching, the robot was able to generate adequate ball handling motor sequences situated to the relative position between the robot's hands and the ball. The same scheme was applied to a block handling learning task where it was shown that the robot can switch among learned different block handling sequences, situated to the ways of interaction by human supporters. Our analysis showed that entrainment of the internal memory structures of the RNNPB through the interactions of the objects and the human supporters are the essential mechanisms for those observed situated behaviors of the robot</description><identifier>ISSN: 0893-6080</identifier><identifier>EISSN: 1879-2782</identifier><identifier>DOI: 10.1016/j.neunet.2006.02.007</identifier><identifier>PMID: 16618536</identifier><language>eng</language><publisher>Oxford: Elsevier Ltd</publisher><subject>Algorithms ; Applied sciences ; Artificial Intelligence ; Computer science; control theory; systems ; Computer Simulation ; Connectionism. Neural networks ; Dynamical systems approach ; Exact sciences and technology ; Handling (Psychology) ; Humans ; Learning of object handling behavior ; Memory - physiology ; Models, Neurological ; Neural Networks (Computer) ; Nonlinear Dynamics ; Predictive Value of Tests ; Psychomotor Performance - physiology ; Recurrent neural network</subject><ispartof>Neural networks, 2006-04, Vol.19 (3), p.323-337</ispartof><rights>2006 Elsevier Ltd</rights><rights>2006 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c516t-dd44aad0e3c130b768ac6332f1246bc68c7856cd1814bab44ca89a431e5822aa3</citedby><cites>FETCH-LOGICAL-c516t-dd44aad0e3c130b768ac6332f1246bc68c7856cd1814bab44ca89a431e5822aa3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0893608006000311$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>309,310,314,776,780,785,786,3537,23909,23910,25118,27901,27902,65306</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&amp;idt=17869532$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/16618536$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ito, Masato</creatorcontrib><creatorcontrib>Noda, Kuniaki</creatorcontrib><creatorcontrib>Hoshino, Yukiko</creatorcontrib><creatorcontrib>Tani, Jun</creatorcontrib><title>Dynamic and interactive generation of object handling behaviors by a small humanoid robot using a dynamic neural network model</title><title>Neural networks</title><addtitle>Neural Netw</addtitle><description>This study presents experiments on the learning of object handling behaviors by a small humanoid robot using a dynamic neural network model, the recurrent neural network with parametric bias (RNNPB). The first experiment showed that after the robot learned different types of ball handling behaviors using human direct teaching, the robot was able to generate adequate ball handling motor sequences situated to the relative position between the robot's hands and the ball. The same scheme was applied to a block handling learning task where it was shown that the robot can switch among learned different block handling sequences, situated to the ways of interaction by human supporters. Our analysis showed that entrainment of the internal memory structures of the RNNPB through the interactions of the objects and the human supporters are the essential mechanisms for those observed situated behaviors of the robot</description><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Artificial Intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Computer Simulation</subject><subject>Connectionism. Neural networks</subject><subject>Dynamical systems approach</subject><subject>Exact sciences and technology</subject><subject>Handling (Psychology)</subject><subject>Humans</subject><subject>Learning of object handling behavior</subject><subject>Memory - physiology</subject><subject>Models, Neurological</subject><subject>Neural Networks (Computer)</subject><subject>Nonlinear Dynamics</subject><subject>Predictive Value of Tests</subject><subject>Psychomotor Performance - physiology</subject><subject>Recurrent neural network</subject><issn>0893-6080</issn><issn>1879-2782</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkTuP1DAQgC0E4vYW_gFCbqBL8CNxnAYJHXAgnUQDtTWxJ7deEvuwk0Xb8Nvxale6DpqZKb55aD5CXnFWc8bVu30dcA241IIxVTNRM9Y9IRuuu74SnRZPyYbpXlaKaXZFrnPeswLqRj4nV1wprlupNuTPx2OA2VsKwVEfFkxgF39Aeo-h1IuPgcaRxmGPdqG7Qk0-3NMBd3DwMWU6HCnQPMM00d06Q4je0RSHuNA1n0ig7rKhnJtgKmn5HdNPOkeH0wvybIQp48tL3pIfnz99v_lS3X27_Xrz4a6yLVdL5VzTADiG0nLJhk5psEpKMXLRqMEqbTvdKuu45s0AQ9NY0D00kmOrhQCQW_L2PPchxV8r5sXMPlucJggY12xU1_edaNV_Qd7Lru9K3JLmDNoUc044mofkZ0hHw5k5CTJ7cxZkToIME6YIKm2vL_PXYUb32HQxUoA3FwCyhWlMEKzPj1ynVd9KUbj3Zw7L2w4ek8nWY7DofCqqjIv-35f8BW-4srA</recordid><startdate>20060401</startdate><enddate>20060401</enddate><creator>Ito, Masato</creator><creator>Noda, Kuniaki</creator><creator>Hoshino, Yukiko</creator><creator>Tani, Jun</creator><general>Elsevier Ltd</general><general>Elsevier Science</general><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7TK</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>20060401</creationdate><title>Dynamic and interactive generation of object handling behaviors by a small humanoid robot using a dynamic neural network model</title><author>Ito, Masato ; Noda, Kuniaki ; Hoshino, Yukiko ; Tani, Jun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c516t-dd44aad0e3c130b768ac6332f1246bc68c7856cd1814bab44ca89a431e5822aa3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Algorithms</topic><topic>Applied sciences</topic><topic>Artificial Intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Computer Simulation</topic><topic>Connectionism. Neural networks</topic><topic>Dynamical systems approach</topic><topic>Exact sciences and technology</topic><topic>Handling (Psychology)</topic><topic>Humans</topic><topic>Learning of object handling behavior</topic><topic>Memory - physiology</topic><topic>Models, Neurological</topic><topic>Neural Networks (Computer)</topic><topic>Nonlinear Dynamics</topic><topic>Predictive Value of Tests</topic><topic>Psychomotor Performance - physiology</topic><topic>Recurrent neural network</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ito, Masato</creatorcontrib><creatorcontrib>Noda, Kuniaki</creatorcontrib><creatorcontrib>Hoshino, Yukiko</creatorcontrib><creatorcontrib>Tani, Jun</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Neural networks</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ito, Masato</au><au>Noda, Kuniaki</au><au>Hoshino, Yukiko</au><au>Tani, Jun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Dynamic and interactive generation of object handling behaviors by a small humanoid robot using a dynamic neural network model</atitle><jtitle>Neural networks</jtitle><addtitle>Neural Netw</addtitle><date>2006-04-01</date><risdate>2006</risdate><volume>19</volume><issue>3</issue><spage>323</spage><epage>337</epage><pages>323-337</pages><issn>0893-6080</issn><eissn>1879-2782</eissn><abstract>This study presents experiments on the learning of object handling behaviors by a small humanoid robot using a dynamic neural network model, the recurrent neural network with parametric bias (RNNPB). The first experiment showed that after the robot learned different types of ball handling behaviors using human direct teaching, the robot was able to generate adequate ball handling motor sequences situated to the relative position between the robot's hands and the ball. The same scheme was applied to a block handling learning task where it was shown that the robot can switch among learned different block handling sequences, situated to the ways of interaction by human supporters. Our analysis showed that entrainment of the internal memory structures of the RNNPB through the interactions of the objects and the human supporters are the essential mechanisms for those observed situated behaviors of the robot</abstract><cop>Oxford</cop><pub>Elsevier Ltd</pub><pmid>16618536</pmid><doi>10.1016/j.neunet.2006.02.007</doi><tpages>15</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0893-6080
ispartof Neural networks, 2006-04, Vol.19 (3), p.323-337
issn 0893-6080
1879-2782
language eng
recordid cdi_proquest_miscellaneous_67997256
source MEDLINE; Elsevier ScienceDirect Journals
subjects Algorithms
Applied sciences
Artificial Intelligence
Computer science
control theory
systems
Computer Simulation
Connectionism. Neural networks
Dynamical systems approach
Exact sciences and technology
Handling (Psychology)
Humans
Learning of object handling behavior
Memory - physiology
Models, Neurological
Neural Networks (Computer)
Nonlinear Dynamics
Predictive Value of Tests
Psychomotor Performance - physiology
Recurrent neural network
title Dynamic and interactive generation of object handling behaviors by a small humanoid robot using a dynamic neural network model
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-13T03%3A55%3A20IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Dynamic%20and%20interactive%20generation%20of%20object%20handling%20behaviors%20by%20a%20small%20humanoid%20robot%20using%20a%20dynamic%20neural%20network%20model&rft.jtitle=Neural%20networks&rft.au=Ito,%20Masato&rft.date=2006-04-01&rft.volume=19&rft.issue=3&rft.spage=323&rft.epage=337&rft.pages=323-337&rft.issn=0893-6080&rft.eissn=1879-2782&rft_id=info:doi/10.1016/j.neunet.2006.02.007&rft_dat=%3Cproquest_cross%3E19379719%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=19379719&rft_id=info:pmid/16618536&rft_els_id=S0893608006000311&rfr_iscdi=true