Salient Feature of Haptic-Based Guidance of People in Low Visibility Environments Using Hard Reins.
Identifieur interne : 000327 ( PubMed/Corpus ); précédent : 000326; suivant : 000328Salient Feature of Haptic-Based Guidance of People in Low Visibility Environments Using Hard Reins.
Auteurs : Anuradha Ranasinghe ; Nantachai Sornkarn ; Prokar Dasgupta ; Kaspar Althoefer ; Jacques Penders ; Thrishantha NanayakkaraSource :
- IEEE transactions on cybernetics [ 2168-2275 ] ; 2016.
Abstract
This paper presents salient features of human-human interaction where one person with limited auditory and visual perception of the environment (a follower) is guided by an agent with full perceptual capabilities (a guider) via a hard rein along a given path. We investigate several salient features of the interaction between the guider and follower such as: 1) the order of an autoregressive (AR) control policy that maps states of the follower to actions of the guider; 2) how the guider may modulate the pulling force in response to the trust level of the follower; and 3) how learning may successively apportion the responsibility of control across different muscles of the guider. Based on experimental systems identification on human demonstrations from ten pairs of naive subjects, we show that guiders tend to adopt a third-order AR predictive control policy and followers tend to adopt second-order reactive control policy. Moreover, the extracted guider's control policy was implemented and validated by human-robot interaction experiments. By modeling the follower's dynamics with a time varying virtual damped inertial system, we found that it is the coefficient of virtual damping which is most sensitive to the trust level of the follower. We used these experimental insights to derive a novel controller that integrates an optimal order control policy with a push/pull force modulator in response to the trust level of the follower monitored using a time varying virtual damped inertial model.
DOI: 10.1109/TCYB.2015.2409772
PubMed: 26080390
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We investigate several salient features of the interaction between the guider and follower such as: 1) the order of an autoregressive (AR) control policy that maps states of the follower to actions of the guider; 2) how the guider may modulate the pulling force in response to the trust level of the follower; and 3) how learning may successively apportion the responsibility of control across different muscles of the guider. Based on experimental systems identification on human demonstrations from ten pairs of naive subjects, we show that guiders tend to adopt a third-order AR predictive control policy and followers tend to adopt second-order reactive control policy. Moreover, the extracted guider's control policy was implemented and validated by human-robot interaction experiments. By modeling the follower's dynamics with a time varying virtual damped inertial system, we found that it is the coefficient of virtual damping which is most sensitive to the trust level of the follower. We used these experimental insights to derive a novel controller that integrates an optimal order control policy with a push/pull force modulator in response to the trust level of the follower monitored using a time varying virtual damped inertial model.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="In-Process"><PMID Version="1">26080390</PMID><DateCreated><Year>2016</Year><Month>05</Month><Day>10</Day></DateCreated><DateRevised><Year>2016</Year><Month>05</Month><Day>11</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">2168-2275</ISSN><JournalIssue CitedMedium="Internet"><Volume>46</Volume><Issue>2</Issue><PubDate><Year>2016</Year><Month>Feb</Month></PubDate></JournalIssue><Title>IEEE transactions on cybernetics</Title><ISOAbbreviation>IEEE Trans Cybern</ISOAbbreviation></Journal><ArticleTitle>Salient Feature of Haptic-Based Guidance of People in Low Visibility Environments Using Hard Reins.</ArticleTitle><Pagination><MedlinePgn>568-79</MedlinePgn></Pagination><Abstract><AbstractText>This paper presents salient features of human-human interaction where one person with limited auditory and visual perception of the environment (a follower) is guided by an agent with full perceptual capabilities (a guider) via a hard rein along a given path. We investigate several salient features of the interaction between the guider and follower such as: 1) the order of an autoregressive (AR) control policy that maps states of the follower to actions of the guider; 2) how the guider may modulate the pulling force in response to the trust level of the follower; and 3) how learning may successively apportion the responsibility of control across different muscles of the guider. Based on experimental systems identification on human demonstrations from ten pairs of naive subjects, we show that guiders tend to adopt a third-order AR predictive control policy and followers tend to adopt second-order reactive control policy. Moreover, the extracted guider's control policy was implemented and validated by human-robot interaction experiments. By modeling the follower's dynamics with a time varying virtual damped inertial system, we found that it is the coefficient of virtual damping which is most sensitive to the trust level of the follower. We used these experimental insights to derive a novel controller that integrates an optimal order control policy with a push/pull force modulator in response to the trust level of the follower monitored using a time varying virtual damped inertial model.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Ranasinghe</LastName><ForeName>Anuradha</ForeName><Initials>A</Initials></Author><Author ValidYN="Y"><LastName>Sornkarn</LastName><ForeName>Nantachai</ForeName><Initials>N</Initials></Author><Author ValidYN="Y"><LastName>Dasgupta</LastName><ForeName>Prokar</ForeName><Initials>P</Initials></Author><Author ValidYN="Y"><LastName>Althoefer</LastName><ForeName>Kaspar</ForeName><Initials>K</Initials></Author><Author ValidYN="Y"><LastName>Penders</LastName><ForeName>Jacques</ForeName><Initials>J</Initials></Author><Author ValidYN="Y"><LastName>Nanayakkara</LastName><ForeName>Thrishantha</ForeName><Initials>T</Initials></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>MR/J006742/1</GrantID><Agency>Medical Research Council</Agency><Country>United Kingdom</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>IEEE Trans Cybern</MedlineTA><NlmUniqueID>101609393</NlmUniqueID><ISSNLinking>2168-2267</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2015</Year><Month>6</Month><Day>17</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2015</Year><Month>6</Month><Day>17</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2015</Year><Month>6</Month><Day>17</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="doi">10.1109/TCYB.2015.2409772</ArticleId><ArticleId IdType="pubmed">26080390</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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