A Probabilistic Framework to Detect Suitable Grasping Regions on Objects
Autor(a) principal: | |
---|---|
Data de Publicação: | 2012 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/92465 https://doi.org/10.3182/20120905-3-HR-2030.00090 |
Resumo: | This work relies on a probabilistic framework to search for suitable grasping regions on objects. In this approach, the object model is acquired based on occupancy grid representation that deals with the sensor uncertainty allowing later the decomposition of the object global shape into components. Through mixture distribution-based representation we achieve the object segmentation where the outputs are the point cloud clustering. Each object component is matched to a geometrical primitive. The advantage of representing object components into geometrical primitives is due to the simplification and approximation of the shape that facilitates the search for suitable object region for grasping given a context. Human demonstrations of predefined grasp are recorded and then overlaid on the object surface given by the probabilistic volumetric map to find the contact points of stable grasps. By observing the human choice during the object grasping, we perform the learning phase. Bayesian theory is used to identify a potential object region for grasping in a specific context when the artificial system faces a new object that is taken as a familiar object due to the primitives approximation into known components. |
id |
RCAP_ea4cb0740615afc28a996b1504e44c76 |
---|---|
oai_identifier_str |
oai:estudogeral.uc.pt:10316/92465 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
A Probabilistic Framework to Detect Suitable Grasping Regions on ObjectsThis work relies on a probabilistic framework to search for suitable grasping regions on objects. In this approach, the object model is acquired based on occupancy grid representation that deals with the sensor uncertainty allowing later the decomposition of the object global shape into components. Through mixture distribution-based representation we achieve the object segmentation where the outputs are the point cloud clustering. Each object component is matched to a geometrical primitive. The advantage of representing object components into geometrical primitives is due to the simplification and approximation of the shape that facilitates the search for suitable object region for grasping given a context. Human demonstrations of predefined grasp are recorded and then overlaid on the object surface given by the probabilistic volumetric map to find the contact points of stable grasps. By observing the human choice during the object grasping, we perform the learning phase. Bayesian theory is used to identify a potential object region for grasping in a specific context when the artificial system faces a new object that is taken as a familiar object due to the primitives approximation into known components.2012info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/92465http://hdl.handle.net/10316/92465https://doi.org/10.3182/20120905-3-HR-2030.00090eng14746670https://www.sciencedirect.com/science/article/pii/S1474667016336187Faria, Diego R.Martins, Ricardo Filipe AlvesLobo, JorgeDias, Jorgeinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2022-05-25T03:03:09Zoai:estudogeral.uc.pt:10316/92465Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:11:33.202631Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A Probabilistic Framework to Detect Suitable Grasping Regions on Objects |
title |
A Probabilistic Framework to Detect Suitable Grasping Regions on Objects |
spellingShingle |
A Probabilistic Framework to Detect Suitable Grasping Regions on Objects Faria, Diego R. |
title_short |
A Probabilistic Framework to Detect Suitable Grasping Regions on Objects |
title_full |
A Probabilistic Framework to Detect Suitable Grasping Regions on Objects |
title_fullStr |
A Probabilistic Framework to Detect Suitable Grasping Regions on Objects |
title_full_unstemmed |
A Probabilistic Framework to Detect Suitable Grasping Regions on Objects |
title_sort |
A Probabilistic Framework to Detect Suitable Grasping Regions on Objects |
author |
Faria, Diego R. |
author_facet |
Faria, Diego R. Martins, Ricardo Filipe Alves Lobo, Jorge Dias, Jorge |
author_role |
author |
author2 |
Martins, Ricardo Filipe Alves Lobo, Jorge Dias, Jorge |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Faria, Diego R. Martins, Ricardo Filipe Alves Lobo, Jorge Dias, Jorge |
description |
This work relies on a probabilistic framework to search for suitable grasping regions on objects. In this approach, the object model is acquired based on occupancy grid representation that deals with the sensor uncertainty allowing later the decomposition of the object global shape into components. Through mixture distribution-based representation we achieve the object segmentation where the outputs are the point cloud clustering. Each object component is matched to a geometrical primitive. The advantage of representing object components into geometrical primitives is due to the simplification and approximation of the shape that facilitates the search for suitable object region for grasping given a context. Human demonstrations of predefined grasp are recorded and then overlaid on the object surface given by the probabilistic volumetric map to find the contact points of stable grasps. By observing the human choice during the object grasping, we perform the learning phase. Bayesian theory is used to identify a potential object region for grasping in a specific context when the artificial system faces a new object that is taken as a familiar object due to the primitives approximation into known components. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10316/92465 http://hdl.handle.net/10316/92465 https://doi.org/10.3182/20120905-3-HR-2030.00090 |
url |
http://hdl.handle.net/10316/92465 https://doi.org/10.3182/20120905-3-HR-2030.00090 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
14746670 https://www.sciencedirect.com/science/article/pii/S1474667016336187 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799134012109750272 |