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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection [[electronic resource] /] / by Xuefeng Zhou, Hongmin Wu, Juan Rojas, Zhihao Xu, Shuai Li
| Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection [[electronic resource] /] / by Xuefeng Zhou, Hongmin Wu, Juan Rojas, Zhihao Xu, Shuai Li |
| Autore | Zhou Xuefeng |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Springer Nature, 2020 |
| Descrizione fisica | 1 online resource (XVII, 137 p. 50 illus., 44 illus. in color.) |
| Disciplina | 629.892 |
| Soggetto topico |
Robotics
Automation Statistics Control engineering Mechatronics Machine learning Mathematical models Robotics and Automation Bayesian Inference Control, Robotics, Mechatronics Machine Learning Mathematical Modeling and Industrial Mathematics |
| Soggetto non controllato |
Robotics and Automation
Bayesian Inference Control, Robotics, Mechatronics Machine Learning Mathematical Modeling and Industrial Mathematics Robotic Engineering Control, Robotics, Automation Collaborative Robot Introspection Nonparametric Bayesian Inference Anomaly Monitoring and Diagnosis Multimodal Perception Anomaly Recovery Human-robot Collaboration Robot Safety and Protection Hidden Markov Model Robot Autonomous Manipulation open access Robotics Bayesian inference Automatic control engineering Electronic devices & materials Machine learning Mathematical modelling Maths for engineers |
| ISBN | 981-15-6263-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction to Robot Introspection -- Nonparametric Bayesian Modeling of Multimodal Time Series -- Incremental Learning Robot Complex Task Representation and Identification -- Nonparametric Bayesian Method for Robot Anomaly Monitoring -- Nonparametric Bayesian Method for Robot Anomaly Diagnose -- Learning Policy for Robot Anomaly Recovery based on Robot. |
| Record Nr. | UNISA-996418268803316 |
Zhou Xuefeng
|
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| Springer Nature, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection / / by Xuefeng Zhou, Hongmin Wu, Juan Rojas, Zhihao Xu, Shuai Li
| Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection / / by Xuefeng Zhou, Hongmin Wu, Juan Rojas, Zhihao Xu, Shuai Li |
| Autore | Zhou Xuefeng |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Springer Nature, 2020 |
| Descrizione fisica | 1 online resource (XVII, 137 p. 50 illus., 44 illus. in color.) |
| Disciplina | 629.892 |
| Soggetto topico |
Robotics
Automation Statistics Control engineering Mechatronics Machine learning Mathematical models Robotics and Automation Bayesian Inference Control, Robotics, Mechatronics Machine Learning Mathematical Modeling and Industrial Mathematics |
| Soggetto non controllato |
Robotics and Automation
Bayesian Inference Control, Robotics, Mechatronics Machine Learning Mathematical Modeling and Industrial Mathematics Robotic Engineering Control, Robotics, Automation Collaborative Robot Introspection Nonparametric Bayesian Inference Anomaly Monitoring and Diagnosis Multimodal Perception Anomaly Recovery Human-robot Collaboration Robot Safety and Protection Hidden Markov Model Robot Autonomous Manipulation open access Robotics Bayesian inference Automatic control engineering Electronic devices & materials Machine learning Mathematical modelling Maths for engineers |
| ISBN | 981-15-6263-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction to Robot Introspection -- Nonparametric Bayesian Modeling of Multimodal Time Series -- Incremental Learning Robot Complex Task Representation and Identification -- Nonparametric Bayesian Method for Robot Anomaly Monitoring -- Nonparametric Bayesian Method for Robot Anomaly Diagnose -- Learning Policy for Robot Anomaly Recovery based on Robot. |
| Record Nr. | UNINA-9910416119103321 |
|
Zhou Xuefeng
|
||
| Springer Nature, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||