Computational Design of Passive Grippers

Milin Kodnongbua, Ian Good, Yu Lou, Jeffrey Lipton, Adriana Schulz

University of Washington

In ACM Transactions on Graphics (Proceedings of SIGGRAPH), 2022 (Awaiting publication)


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.


Paper, PDF (50 MB) Paper (compressed), PDF (18 MB) Code and Data, Github

Fast Forward

Full Talk

Supplemental Video


This work was funded by NSF grants 2035717, 1954028, 3922035717, and 2017927; the Office of Naval Research grant DB2240; and the Murdock Foundation Materials Foundry grant 201913596. Special thanks to Tyler Freitas for his assistance in data collection.