Deformability-Based Grasp Pose Detection From a Visible Image
In this study, we introduce a method for estimating deformability from the appearance of an object and a technique for detecting the grasp pose using a single-depth image. Deformability, defined as a score, provides an approximate measure of the ease with which an object can be deformed, based on th...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10776971/ |
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| author | Koshi Makihara Yukiyasu Domae Ryo Hanai Ixchel G. Ramirez-Alpizar Hirokatsu Kataoka Kensuke Harada |
| author_facet | Koshi Makihara Yukiyasu Domae Ryo Hanai Ixchel G. Ramirez-Alpizar Hirokatsu Kataoka Kensuke Harada |
| author_sort | Koshi Makihara |
| collection | DOAJ |
| description | In this study, we introduce a method for estimating deformability from the appearance of an object and a technique for detecting the grasp pose using a single-depth image. Deformability, defined as a score, provides an approximate measure of the ease with which an object can be deformed, based on the intuitive human assumption that appearance correlates with this property. Unlike previous approaches that synthesize the entire image in one step and struggle with accuracy in complex scenarios, such as cluttered environments with multiple objects, our method advances the field by employing an encoder-decoder model for simultaneous deformability estimation and instance segmentation from depth images, as well as by enhancing grasp pose detection for deformable objects under specific conditions using the outcomes of our deformability assessments. We demonstrated the efficacy of our integrated approach in diverse challenging settings, including the manipulation of deformable hollow objects in cluttered scenes, interaction with partially deformable objects, and displacement of nearby deformable objects in real-world environments. |
| format | Article |
| id | doaj-art-629e0a06cd124075a69e3b6cb8a5885d |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-629e0a06cd124075a69e3b6cb8a5885d2025-08-20T02:32:16ZengIEEEIEEE Access2169-35362024-01-011219064019064910.1109/ACCESS.2024.351154610776971Deformability-Based Grasp Pose Detection From a Visible ImageKoshi Makihara0https://orcid.org/0000-0003-4145-7595Yukiyasu Domae1https://orcid.org/0000-0002-1366-9657Ryo Hanai2https://orcid.org/0000-0003-4216-4866Ixchel G. Ramirez-Alpizar3https://orcid.org/0000-0002-7805-7539Hirokatsu Kataoka4https://orcid.org/0000-0001-8844-165XKensuke Harada5https://orcid.org/0000-0002-7576-756XGraduate School of Engineering Science, Osaka University, Osaka, JapanNational Institute of Advanced Industrial Science and Technology, Tokyo, JapanNational Institute of Advanced Industrial Science and Technology, Tokyo, JapanNational Institute of Advanced Industrial Science and Technology, Tokyo, JapanNational Institute of Advanced Industrial Science and Technology, Tokyo, JapanGraduate School of Engineering Science, Osaka University, Osaka, JapanIn this study, we introduce a method for estimating deformability from the appearance of an object and a technique for detecting the grasp pose using a single-depth image. Deformability, defined as a score, provides an approximate measure of the ease with which an object can be deformed, based on the intuitive human assumption that appearance correlates with this property. Unlike previous approaches that synthesize the entire image in one step and struggle with accuracy in complex scenarios, such as cluttered environments with multiple objects, our method advances the field by employing an encoder-decoder model for simultaneous deformability estimation and instance segmentation from depth images, as well as by enhancing grasp pose detection for deformable objects under specific conditions using the outcomes of our deformability assessments. We demonstrated the efficacy of our integrated approach in diverse challenging settings, including the manipulation of deformable hollow objects in cluttered scenes, interaction with partially deformable objects, and displacement of nearby deformable objects in real-world environments.https://ieeexplore.ieee.org/document/10776971/Graspingdeformable objectsdeformability estimation |
| spellingShingle | Koshi Makihara Yukiyasu Domae Ryo Hanai Ixchel G. Ramirez-Alpizar Hirokatsu Kataoka Kensuke Harada Deformability-Based Grasp Pose Detection From a Visible Image IEEE Access Grasping deformable objects deformability estimation |
| title | Deformability-Based Grasp Pose Detection From a Visible Image |
| title_full | Deformability-Based Grasp Pose Detection From a Visible Image |
| title_fullStr | Deformability-Based Grasp Pose Detection From a Visible Image |
| title_full_unstemmed | Deformability-Based Grasp Pose Detection From a Visible Image |
| title_short | Deformability-Based Grasp Pose Detection From a Visible Image |
| title_sort | deformability based grasp pose detection from a visible image |
| topic | Grasping deformable objects deformability estimation |
| url | https://ieeexplore.ieee.org/document/10776971/ |
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