Application of Bayesian Optimization in Gripper Design for Effective Grasping
Despite many recent technological advancements, grasping remains a challenging open problem in robotic manipulation. In contrast with most research which focuses equipping grippers with varying degree of intelligence, we approach grasping from a gripper design perspective, aiming to find the best to...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10838536/ |
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| author | Marco Todescato Dominik T. Matt Andrea Giusti |
| author_facet | Marco Todescato Dominik T. Matt Andrea Giusti |
| author_sort | Marco Todescato |
| collection | DOAJ |
| description | Despite many recent technological advancements, grasping remains a challenging open problem in robotic manipulation. In contrast with most research which focuses equipping grippers with varying degree of intelligence, we approach grasping from a gripper design perspective, aiming to find the best tool for grasping a specific set of objects. Building on our previous work, this paper reviews a suitable parametrization for the geometry of two common families of industrial grippers and presents a grasp score beneficial for gripper design. We then formally cast the problem of finding the best gripper parametrization within a probabilistic framework, addressing it using Bayesian Optimization tools. Numerical results on a set of industrial objects demonstrate the effectiveness of the approach showing up to <inline-formula> <tex-math notation="LaTeX">$\approx 300 \%$ </tex-math></inline-formula> improvement compared to the performance obtained using a fixed set of grippers. |
| format | Article |
| id | doaj-art-622a877dbc44482ea0680e28d4028370 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-622a877dbc44482ea0680e28d40283702025-01-21T00:02:15ZengIEEEIEEE Access2169-35362025-01-0113102151022610.1109/ACCESS.2025.352864310838536Application of Bayesian Optimization in Gripper Design for Effective GraspingMarco Todescato0https://orcid.org/0000-0003-1449-5692Dominik T. Matt1https://orcid.org/0000-0002-2365-7529Andrea Giusti2https://orcid.org/0000-0003-1275-8161Fraunhofer Italia Research Scarl, Bolzano, ItalyFraunhofer Italia Research Scarl, Bolzano, ItalyFraunhofer Italia Research Scarl, Bolzano, ItalyDespite many recent technological advancements, grasping remains a challenging open problem in robotic manipulation. In contrast with most research which focuses equipping grippers with varying degree of intelligence, we approach grasping from a gripper design perspective, aiming to find the best tool for grasping a specific set of objects. Building on our previous work, this paper reviews a suitable parametrization for the geometry of two common families of industrial grippers and presents a grasp score beneficial for gripper design. We then formally cast the problem of finding the best gripper parametrization within a probabilistic framework, addressing it using Bayesian Optimization tools. Numerical results on a set of industrial objects demonstrate the effectiveness of the approach showing up to <inline-formula> <tex-math notation="LaTeX">$\approx 300 \%$ </tex-math></inline-formula> improvement compared to the performance obtained using a fixed set of grippers.https://ieeexplore.ieee.org/document/10838536/Artificial intelligenceautomationroboticsmanufacturingoptimization |
| spellingShingle | Marco Todescato Dominik T. Matt Andrea Giusti Application of Bayesian Optimization in Gripper Design for Effective Grasping IEEE Access Artificial intelligence automation robotics manufacturing optimization |
| title | Application of Bayesian Optimization in Gripper Design for Effective Grasping |
| title_full | Application of Bayesian Optimization in Gripper Design for Effective Grasping |
| title_fullStr | Application of Bayesian Optimization in Gripper Design for Effective Grasping |
| title_full_unstemmed | Application of Bayesian Optimization in Gripper Design for Effective Grasping |
| title_short | Application of Bayesian Optimization in Gripper Design for Effective Grasping |
| title_sort | application of bayesian optimization in gripper design for effective grasping |
| topic | Artificial intelligence automation robotics manufacturing optimization |
| url | https://ieeexplore.ieee.org/document/10838536/ |
| work_keys_str_mv | AT marcotodescato applicationofbayesianoptimizationingripperdesignforeffectivegrasping AT dominiktmatt applicationofbayesianoptimizationingripperdesignforeffectivegrasping AT andreagiusti applicationofbayesianoptimizationingripperdesignforeffectivegrasping |