Reinforcement learning for human-robot shared control
Purpose This paper aims to propose a general framework of shared control for human–robot interaction. Design/methodology/approach Human dynamics are considered in analysis of the coupled human–robot system. Motion intentions of both human and robot are taken into account in the control objective of...
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| Veröffentlicht in: | Assembly automation 2020-02, Vol.40 (1), p.105-117 |
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| container_title | Assembly automation |
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| creator | Li, Yanan Tee, Keng Peng Yan, Rui Ge, Shuzhi Sam |
| description | Purpose
This paper aims to propose a general framework of shared control for human–robot interaction.
Design/methodology/approach
Human dynamics are considered in analysis of the coupled human–robot system. Motion intentions of both human and robot are taken into account in the control objective of the robot. Reinforcement learning is developed to achieve the control objective subject to unknown dynamics of human and robot. The closed-loop system performance is discussed through a rigorous proof.
Findings
Simulations are conducted to demonstrate the learning capability of the proposed method and its feasibility in handling various situations.
Originality/value
Compared to existing works, the proposed framework combines motion intentions of both human and robot in a human–robot shared control system, without the requirement of the knowledge of human’s and robot’s dynamics. |
| doi_str_mv | 10.1108/AA-10-2018-0153 |
| format | Article |
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This paper aims to propose a general framework of shared control for human–robot interaction.
Design/methodology/approach
Human dynamics are considered in analysis of the coupled human–robot system. Motion intentions of both human and robot are taken into account in the control objective of the robot. Reinforcement learning is developed to achieve the control objective subject to unknown dynamics of human and robot. The closed-loop system performance is discussed through a rigorous proof.
Findings
Simulations are conducted to demonstrate the learning capability of the proposed method and its feasibility in handling various situations.
Originality/value
Compared to existing works, the proposed framework combines motion intentions of both human and robot in a human–robot shared control system, without the requirement of the knowledge of human’s and robot’s dynamics.</description><identifier>ISSN: 0144-5154</identifier><identifier>ISSN: 2754-6969</identifier><identifier>EISSN: 1758-4078</identifier><identifier>EISSN: 2754-6977</identifier><identifier>DOI: 10.1108/AA-10-2018-0153</identifier><language>eng</language><publisher>Bingley: Emerald Publishing Limited</publisher><subject>Feedback control ; Human motion ; Human performance ; Kinematics ; Learning ; Performance evaluation ; Robot control ; Robot dynamics ; Robots</subject><ispartof>Assembly automation, 2020-02, Vol.40 (1), p.105-117</ispartof><rights>Emerald Publishing Limited</rights><rights>Emerald Publishing Limited 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c349t-a279aa3fa645792e11c8b0e43bb4a477f634ec199cbdda1f6869010a508ac45f3</citedby><cites>FETCH-LOGICAL-c349t-a279aa3fa645792e11c8b0e43bb4a477f634ec199cbdda1f6869010a508ac45f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.emerald.com/insight/content/doi/10.1108/AA-10-2018-0153/full/html$$EHTML$$P50$$Gemerald$$H</linktohtml><link.rule.ids>314,780,784,966,11634,27923,27924,52688</link.rule.ids></links><search><creatorcontrib>Li, Yanan</creatorcontrib><creatorcontrib>Tee, Keng Peng</creatorcontrib><creatorcontrib>Yan, Rui</creatorcontrib><creatorcontrib>Ge, Shuzhi Sam</creatorcontrib><title>Reinforcement learning for human-robot shared control</title><title>Assembly automation</title><description>Purpose
This paper aims to propose a general framework of shared control for human–robot interaction.
Design/methodology/approach
Human dynamics are considered in analysis of the coupled human–robot system. Motion intentions of both human and robot are taken into account in the control objective of the robot. Reinforcement learning is developed to achieve the control objective subject to unknown dynamics of human and robot. The closed-loop system performance is discussed through a rigorous proof.
Findings
Simulations are conducted to demonstrate the learning capability of the proposed method and its feasibility in handling various situations.
Originality/value
Compared to existing works, the proposed framework combines motion intentions of both human and robot in a human–robot shared control system, without the requirement of the knowledge of human’s and robot’s dynamics.</description><subject>Feedback control</subject><subject>Human motion</subject><subject>Human performance</subject><subject>Kinematics</subject><subject>Learning</subject><subject>Performance evaluation</subject><subject>Robot control</subject><subject>Robot dynamics</subject><subject>Robots</subject><issn>0144-5154</issn><issn>2754-6969</issn><issn>1758-4078</issn><issn>2754-6977</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNptkE1LAzEQhoMoWKtnrwue087kYzd7XIpaoSCInkM2m9iWbVKT7cF_7y71IniaYXifGeYh5B5hgQhq2TQUgTJARQElvyAzrKSiAip1SWaAQlCJUlyTm5z3ACPD2IzIN7cLPibrDi4MRe9MCrvwWYyjYns6mEBTbONQ5K1JritsDEOK_S258qbP7u63zsnH0-P7ak03r88vq2ZDLRf1QA2ramO4N6WQVc0colUtOMHbVhhRVb7kwlmsa9t2nUFfqrIGBCNBGSuk53PycN57TPHr5PKg9_GUwnhSM8lFyaQEPqaW55RNMefkvD6m3cGkb42gJze6aaZ2cqMnNyOxOBPj18n03T_AH5n8B3KGY2g</recordid><startdate>20200218</startdate><enddate>20200218</enddate><creator>Li, Yanan</creator><creator>Tee, Keng Peng</creator><creator>Yan, Rui</creator><creator>Ge, Shuzhi Sam</creator><general>Emerald Publishing Limited</general><general>Emerald Group Publishing Limited</general><scope>AAYXX</scope><scope>CITATION</scope><scope>0U~</scope><scope>1-H</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F28</scope><scope>FR3</scope><scope>F~G</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K6~</scope><scope>L.-</scope><scope>L.0</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0C</scope><scope>M7S</scope><scope>PQBIZ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>Q9U</scope><scope>S0W</scope></search><sort><creationdate>20200218</creationdate><title>Reinforcement learning for human-robot shared control</title><author>Li, Yanan ; Tee, Keng Peng ; Yan, Rui ; Ge, Shuzhi Sam</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c349t-a279aa3fa645792e11c8b0e43bb4a477f634ec199cbdda1f6869010a508ac45f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Feedback control</topic><topic>Human motion</topic><topic>Human performance</topic><topic>Kinematics</topic><topic>Learning</topic><topic>Performance evaluation</topic><topic>Robot control</topic><topic>Robot dynamics</topic><topic>Robots</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Yanan</creatorcontrib><creatorcontrib>Tee, Keng Peng</creatorcontrib><creatorcontrib>Yan, Rui</creatorcontrib><creatorcontrib>Ge, Shuzhi Sam</creatorcontrib><collection>CrossRef</collection><collection>Global News & ABI/Inform Professional</collection><collection>Trade PRO</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection (ProQuest)</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Professional Standard</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ABI/INFORM Global</collection><collection>Engineering Database</collection><collection>ProQuest One Business</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>ProQuest Central Basic</collection><collection>DELNET Engineering & Technology Collection</collection><jtitle>Assembly automation</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Yanan</au><au>Tee, Keng Peng</au><au>Yan, Rui</au><au>Ge, Shuzhi Sam</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Reinforcement learning for human-robot shared control</atitle><jtitle>Assembly automation</jtitle><date>2020-02-18</date><risdate>2020</risdate><volume>40</volume><issue>1</issue><spage>105</spage><epage>117</epage><pages>105-117</pages><issn>0144-5154</issn><issn>2754-6969</issn><eissn>1758-4078</eissn><eissn>2754-6977</eissn><abstract>Purpose
This paper aims to propose a general framework of shared control for human–robot interaction.
Design/methodology/approach
Human dynamics are considered in analysis of the coupled human–robot system. Motion intentions of both human and robot are taken into account in the control objective of the robot. Reinforcement learning is developed to achieve the control objective subject to unknown dynamics of human and robot. The closed-loop system performance is discussed through a rigorous proof.
Findings
Simulations are conducted to demonstrate the learning capability of the proposed method and its feasibility in handling various situations.
Originality/value
Compared to existing works, the proposed framework combines motion intentions of both human and robot in a human–robot shared control system, without the requirement of the knowledge of human’s and robot’s dynamics.</abstract><cop>Bingley</cop><pub>Emerald Publishing Limited</pub><doi>10.1108/AA-10-2018-0153</doi><tpages>13</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0144-5154 |
| ispartof | Assembly automation, 2020-02, Vol.40 (1), p.105-117 |
| issn | 0144-5154 2754-6969 1758-4078 2754-6977 |
| language | eng |
| recordid | cdi_proquest_journals_2534625503 |
| source | Emerald Journals |
| subjects | Feedback control Human motion Human performance Kinematics Learning Performance evaluation Robot control Robot dynamics Robots |
| title | Reinforcement learning for human-robot shared control |
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