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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10838536/ |
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| Summary: | 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. |
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| ISSN: | 2169-3536 |