How to add new knowledge to already trained deep learning models applied to semantic localization
The capacity of a robot to automatically adapt to new environments is crucial, especially in social robotics. Often, when these robots are deployed in home or office environments, they tend to fail because they lack the ability to adapt to new and continuously changing scenarios. In order to accompl...
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| Materyal Türü: | article |
| Dil: | İngilizce |
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2020
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| Online Erişim: | https://link.springer.com/article/10.1007/s10489-019-01517-1 https://ridda2.utp.ac.pa/handle/123456789/9445 |
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| _version_ | 1869652471282925568 |
|---|---|
| author | Cruz, Edmanuel |
| author2 | Rangel, José Carlos Gomez Donoso, Francisco Cazorla, Miguel |
| author2_role | author author author |
| author_browse | Cazorla, Miguel Cruz, Edmanuel Gomez Donoso, Francisco Rangel, José Carlos |
| author_facet | Cruz, Edmanuel Rangel, José Carlos Gomez Donoso, Francisco Cazorla, Miguel |
| author_role | author |
| collection | Repositorio Institucional de documento digitales de acceso abierto de la UTP |
| dc.creator.none.fl_str_mv | Cruz, Edmanuel Rangel, José Carlos Gomez Donoso, Francisco Cazorla, Miguel |
| dc.date.none.fl_str_mv | 06/19/2019 06/19/2019 2020-01-02T21:25:41Z 2020-01-02T21:25:41Z 2020-01-02T21:25:41Z 2020-01-02T21:25:41Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | https://link.springer.com/article/10.1007/s10489-019-01517-1 https://ridda2.utp.ac.pa/handle/123456789/9445 https://ridda2.utp.ac.pa/handle/123456789/9445 |
| dc.language.none.fl_str_mv | eng eng |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/embargoedAccess |
| dc.source.none.fl_str_mv | reponame:Repositorio Institucional de documento digitales de acceso abierto de la UTP instname:Universidad Tecnológica de Panamá instacron:U Tecnológica de Panamá |
| dc.subject.none.fl_str_mv | Semantic localization Deep learning Retraining strategies Machine learning Semantic localization Deep learning Retraining strategies Machine learning |
| dc.title.none.fl_str_mv | How to add new knowledge to already trained deep learning models applied to semantic localization |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| description | The capacity of a robot to automatically adapt to new environments is crucial, especially in social robotics. Often, when these robots are deployed in home or office environments, they tend to fail because they lack the ability to adapt to new and continuously changing scenarios. In order to accomplish this task, robots must obtain new information from the environment, and then add it to their already learned knowledge. Deep learning techniques are often used to tackle this problem successfully. However, these approaches, complete retraining of the models, which is highly time-consuming. In this work, several strategies are tested to find the best way to include new knowledge in an already learned model in a deep learning pipeline, putting the spotlight on the time spent for this training. We tackle the localization problem in the long term with a deep learning approach and testing several retraining strategies. The results of the experiments are discussed and, finally, the best approach is deployed on a Pepper robot. |
| eu_rights_str_mv | embargoedAccess |
| format | article |
| id | lrtest_9a2af4c0c89d0bd89ca3fa4dec21c467 |
| instacron_str | U Tecnológica de Panamá |
| institution | U Tecnológica de Panamá |
| instname_str | Universidad Tecnológica de Panamá |
| language | eng |
| network_acronym_str | lrtest |
| network_name_str | lr |
| oai_identifier_str | oai:ridda2.utp.ac.pa:123456789/9445 |
| publishDate | 2020 |
| publishDateSort | 2020 |
| reponame_str | Repositorio Institucional de documento digitales de acceso abierto de la UTP |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | How to add new knowledge to already trained deep learning models applied to semantic localizationCruz, EdmanuelRangel, José CarlosGomez Donoso, FranciscoCazorla, MiguelSemantic localizationDeep learningRetraining strategiesMachine learningSemantic localizationDeep learningRetraining strategiesMachine learningThe capacity of a robot to automatically adapt to new environments is crucial, especially in social robotics. Often, when these robots are deployed in home or office environments, they tend to fail because they lack the ability to adapt to new and continuously changing scenarios. In order to accomplish this task, robots must obtain new information from the environment, and then add it to their already learned knowledge. Deep learning techniques are often used to tackle this problem successfully. However, these approaches, complete retraining of the models, which is highly time-consuming. In this work, several strategies are tested to find the best way to include new knowledge in an already learned model in a deep learning pipeline, putting the spotlight on the time spent for this training. We tackle the localization problem in the long term with a deep learning approach and testing several retraining strategies. The results of the experiments are discussed and, finally, the best approach is deployed on a Pepper robot.The capacity of a robot to automatically adapt to new environments is crucial, especially in social robotics. Often, when these robots are deployed in home or office environments, they tend to fail because they lack the ability to adapt to new and continuously changing scenarios. In order to accomplish this task, robots must obtain new information from the environment, and then add it to their already learned knowledge. Deep learning techniques are often used to tackle this problem successfully. However, these approaches, complete retraining of the models, which is highly time-consuming. In this work, several strategies are tested to find the best way to include new knowledge in an already learned model in a deep learning pipeline, putting the spotlight on the time spent for this training. We tackle the localization problem in the long term with a deep learning approach and testing several retraining strategies. The results of the experiments are discussed and, finally, the best approach is deployed on a Pepper robot.2020-01-02T21:25:41Z2020-01-02T21:25:41Z2020-01-02T21:25:41Z2020-01-02T21:25:41Z06/19/201906/19/2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://link.springer.com/article/10.1007/s10489-019-01517-1https://ridda2.utp.ac.pa/handle/123456789/9445https://ridda2.utp.ac.pa/handle/123456789/9445engenginfo:eu-repo/semantics/embargoedAccessreponame:Repositorio Institucional de documento digitales de acceso abierto de la UTPinstname:Universidad Tecnológica de Panamáinstacron:U Tecnológica de Panamáoai:ridda2.utp.ac.pa:123456789/94452021-07-06T15:35:10Z |
| spellingShingle | How to add new knowledge to already trained deep learning models applied to semantic localization Cruz, Edmanuel Semantic localization Deep learning Retraining strategies Machine learning Semantic localization Deep learning Retraining strategies Machine learning |
| status_str | publishedVersion |
| title | How to add new knowledge to already trained deep learning models applied to semantic localization |
| title_full | How to add new knowledge to already trained deep learning models applied to semantic localization |
| title_fullStr | How to add new knowledge to already trained deep learning models applied to semantic localization |
| title_full_unstemmed | How to add new knowledge to already trained deep learning models applied to semantic localization |
| title_short | How to add new knowledge to already trained deep learning models applied to semantic localization |
| title_sort | How to add new knowledge to already trained deep learning models applied to semantic localization |
| topic | Semantic localization Deep learning Retraining strategies Machine learning Semantic localization Deep learning Retraining strategies Machine learning |
| url | https://link.springer.com/article/10.1007/s10489-019-01517-1 https://ridda2.utp.ac.pa/handle/123456789/9445 |