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Title: Computational design of passive grippers

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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Award ID(s):
2035717
NSF-PAR ID:
10474801
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
ACM Transactions on Graphics
Date Published:
Journal Name:
ACM Transactions on Graphics
Volume:
41
Issue:
4
ISSN:
0730-0301
Page Range / eLocation ID:
2 to 12
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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