Human-robot interaction control using reinforcement learning:
A comprehensive exploration of the control schemes of human-robot interactions In Human-Robot Interaction Control Using Reinforcement Learning , an expert team of authors delivers a concise overview of human-robot interaction control schemes and insightful presentations of novel, model-free and rein...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, NJ
John Wiley & Sons, Incorporated
[2022]
|
| Schriftenreihe: | IEEE Press series on systems science and engineering
|
| Schlagworte: | |
| Online-Zugang: | FHA01 FHI01 Volltext |
| Zusammenfassung: | A comprehensive exploration of the control schemes of human-robot interactions In Human-Robot Interaction Control Using Reinforcement Learning , an expert team of authors delivers a concise overview of human-robot interaction control schemes and insightful presentations of novel, model-free and reinforcement learning controllers. The book begins with a brief introduction to state-of-the-art human-robot interaction control and reinforcement learning before moving on to describe the typical environment model. The authors also describe some of the most famous identification techniques for parameter estimation. Human-Robot Interaction Control Using Reinforcement Learning offers rigorous mathematical treatments and demonstrations that facilitate the understanding of control schemes and algorithms. It also describes stability and convergence analysis of human-robot interaction control and reinforcement learning based control. The authors also discuss advanced and cutting-edge topics, like inverse and velocity kinematics solutions, H2 neural control, and likely upcoming developments in the field of robotics. Readers will also enjoy: A thorough introduction to model-based human-robot interaction control Comprehensive explorations of model-free human-robot interaction control and human-in-the-loop control using Euler angles Practical discussions of reinforcement learning for robot position and force control, as well as continuous time reinforcement learning for robot force control In-depth examinations of robot control in worst-case uncertainty using reinforcement learning and the control of redundant robots using multi-agent reinforcement learning Perfect for senior undergraduate and graduate students, academic researchers, and industrial practitioners studying and working in the fields of robotics, learning control systems, neural networks, and computational intelligence, Human-Robot Interaction Control Using Reinforcement Learning is also an indispensable resource for students and professionals studying reinforcement learning |
| Beschreibung: | 1 Online-Ressource (xx, 262 Seiten) |
| ISBN: | 9781119782773 9781119782759 9781119782766 |
Internformat
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| 520 | 3 | |a A comprehensive exploration of the control schemes of human-robot interactions In Human-Robot Interaction Control Using Reinforcement Learning , an expert team of authors delivers a concise overview of human-robot interaction control schemes and insightful presentations of novel, model-free and reinforcement learning controllers. The book begins with a brief introduction to state-of-the-art human-robot interaction control and reinforcement learning before moving on to describe the typical environment model. The authors also describe some of the most famous identification techniques for parameter estimation. Human-Robot Interaction Control Using Reinforcement Learning offers rigorous mathematical treatments and demonstrations that facilitate the understanding of control schemes and algorithms. It also describes stability and convergence analysis of human-robot interaction control and reinforcement learning based control. | |
| 520 | 3 | |a The authors also discuss advanced and cutting-edge topics, like inverse and velocity kinematics solutions, H2 neural control, and likely upcoming developments in the field of robotics. | |
| 520 | 3 | |a Readers will also enjoy: A thorough introduction to model-based human-robot interaction control Comprehensive explorations of model-free human-robot interaction control and human-in-the-loop control using Euler angles Practical discussions of reinforcement learning for robot position and force control, as well as continuous time reinforcement learning for robot force control In-depth examinations of robot control in worst-case uncertainty using reinforcement learning and the control of redundant robots using multi-agent reinforcement learning Perfect for senior undergraduate and graduate students, academic researchers, and industrial practitioners studying and working in the fields of robotics, learning control systems, neural networks, and computational intelligence, Human-Robot Interaction Control Using Reinforcement Learning is also an indispensable resource for students and professionals studying reinforcement learning | |
| 653 | 0 | |a Human-robot interaction | |
| 653 | 0 | |a Reinforcement learning | |
| 653 | 0 | |a Intelligent control systems | |
| 653 | 0 | |a Human-robot interaction | |
| 653 | 0 | |a Intelligent control systems | |
| 653 | 0 | |a Reinforcement learning | |
| 700 | 1 | |a Perrusquía, Adolfo |e Verfasser |4 aut | |
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Datensatz im Suchindex
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| author | Yu, Wen Perrusquía, Adolfo |
| author_facet | Yu, Wen Perrusquía, Adolfo |
| author_role | aut aut |
| author_sort | Yu, Wen |
| author_variant | w y wy a p ap |
| building | Verbundindex |
| bvnumber | BV048848976 |
| collection | ZDB-35-WEL ZDB-35-WIC |
| ctrlnum | (OCoLC)1372478351 (DE-599)BVBBV048848976 |
| dewey-full | 629.8924019 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 629 - Other branches of engineering |
| dewey-raw | 629.8924019 |
| dewey-search | 629.8924019 |
| dewey-sort | 3629.8924019 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
| discipline_str_mv | Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
| format | Electronic eBook |
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| id | DE-604.BV048848976 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T21:39:43Z |
| indexdate | 2024-07-10T09:47:45Z |
| institution | BVB |
| isbn | 9781119782773 9781119782759 9781119782766 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034114268 |
| oclc_num | 1372478351 |
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| physical | 1 Online-Ressource (xx, 262 Seiten) |
| psigel | ZDB-35-WEL ZDB-35-WIC ZDB-35-WIC FHA_PDA_WIC_Kauf |
| publishDate | 2022 |
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| publisher | John Wiley & Sons, Incorporated |
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| series2 | IEEE Press series on systems science and engineering |
| spelling | Yu, Wen Verfasser aut Human-robot interaction control using reinforcement learning Wen Yu (CINVESTAV-IPN), Adolfo Perrusquía (CINVESTAV-IPN) Hoboken, NJ John Wiley & Sons, Incorporated [2022] © 2022 1 Online-Ressource (xx, 262 Seiten) txt rdacontent c rdamedia cr rdacarrier IEEE Press series on systems science and engineering A comprehensive exploration of the control schemes of human-robot interactions In Human-Robot Interaction Control Using Reinforcement Learning , an expert team of authors delivers a concise overview of human-robot interaction control schemes and insightful presentations of novel, model-free and reinforcement learning controllers. The book begins with a brief introduction to state-of-the-art human-robot interaction control and reinforcement learning before moving on to describe the typical environment model. The authors also describe some of the most famous identification techniques for parameter estimation. Human-Robot Interaction Control Using Reinforcement Learning offers rigorous mathematical treatments and demonstrations that facilitate the understanding of control schemes and algorithms. It also describes stability and convergence analysis of human-robot interaction control and reinforcement learning based control. The authors also discuss advanced and cutting-edge topics, like inverse and velocity kinematics solutions, H2 neural control, and likely upcoming developments in the field of robotics. Readers will also enjoy: A thorough introduction to model-based human-robot interaction control Comprehensive explorations of model-free human-robot interaction control and human-in-the-loop control using Euler angles Practical discussions of reinforcement learning for robot position and force control, as well as continuous time reinforcement learning for robot force control In-depth examinations of robot control in worst-case uncertainty using reinforcement learning and the control of redundant robots using multi-agent reinforcement learning Perfect for senior undergraduate and graduate students, academic researchers, and industrial practitioners studying and working in the fields of robotics, learning control systems, neural networks, and computational intelligence, Human-Robot Interaction Control Using Reinforcement Learning is also an indispensable resource for students and professionals studying reinforcement learning Human-robot interaction Reinforcement learning Intelligent control systems Perrusquía, Adolfo Verfasser aut Erscheint auch als Druck-Ausgabe Yu, Wen Human-robot interaction control using reinforcement learning Hoboken : Wiley-IEEE Press, 2022 xx, 262 Seiten 9781119782742 1119782740 https://onlinelibrary.wiley.com/doi/book/10.1002/9781119782773 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Yu, Wen Perrusquía, Adolfo Human-robot interaction control using reinforcement learning |
| title | Human-robot interaction control using reinforcement learning |
| title_auth | Human-robot interaction control using reinforcement learning |
| title_exact_search | Human-robot interaction control using reinforcement learning |
| title_exact_search_txtP | Human-robot interaction control using reinforcement learning |
| title_full | Human-robot interaction control using reinforcement learning Wen Yu (CINVESTAV-IPN), Adolfo Perrusquía (CINVESTAV-IPN) |
| title_fullStr | Human-robot interaction control using reinforcement learning Wen Yu (CINVESTAV-IPN), Adolfo Perrusquía (CINVESTAV-IPN) |
| title_full_unstemmed | Human-robot interaction control using reinforcement learning Wen Yu (CINVESTAV-IPN), Adolfo Perrusquía (CINVESTAV-IPN) |
| title_short | Human-robot interaction control using reinforcement learning |
| title_sort | human robot interaction control using reinforcement learning |
| url | https://onlinelibrary.wiley.com/doi/book/10.1002/9781119782773 |
| work_keys_str_mv | AT yuwen humanrobotinteractioncontrolusingreinforcementlearning AT perrusquiaadolfo humanrobotinteractioncontrolusingreinforcementlearning |