Milin Kodnongbua
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The authors have requested minor, non-substantive changes to the Version of Record and, in accordance with ACM policies, a Corrected Version of Record was published on August 1, 2022. For reference purposes, the VoR may still be accessed via the Supplemental Material section on this page.
Abstract
This work proposes a novel generative design tool for passive grippers---robot end effectors that have no additional actuation and instead leverage the existing degrees of freedom in a robotic arm to perform grasping tasks. Passive grippers are used because they offer interesting trade-offs between cost and capabilities. However, existing designs are limited in the types of shapes that can be grasped. This work proposes to use rapid-manufacturing and design optimization to expand the space of shapes that can be passively grasped. Our novel generative design algorithm takes in an object and its positioning with respect to a robotic arm and generates a 3D printable passive gripper that can stably pick the object up. To achieve this, we address the key challenge of jointly optimizing the shape and the insert trajectory to ensure a passively stable grasp. We evaluate our method on a testing suite of 22 objects (23 experiments), all of which were evaluated with physical experiments to bridge the virtual-to-real gap. Code and data are at https://homes.cs.washington.edu/~milink/passive-gripper/
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Version of Record for "Computational design of passive grippers" by Kodnongbua et al., ACM Transactions on Graphics, Volume 41, Issue 4 (TOG 41:4).
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Computational design of passive grippers
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