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.

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Acknowledgement

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.

Citation

@article{Kodnongbua_2022,
    doi = {10.1145/3528223.3530162},
    url = {https://doi.org/10.1145%2F3528223.3530162},
    year = 2022,
    month = {jul},
    publisher = {Association for Computing Machinery ({ACM})},
    volume = {41},
    number = {4},
    pages = {2--12},
    author = {Milin Kodnongbua and Ian Good and Yu Lou and Jeffrey Lipton and Adriana Schulz},
    title = {Computational design of passive grippers},
    journal = {{ACM} Transactions on Graphics}
}