Privacy-Preserving Cloud-Based Grasp Planning

Jeffrey Mahler, Brian Hou, Sherdil Niyaz, Florian T Pokorny, Ramu Chandra, Ken Goldberg
In IEEE CASE, 2016


Support industrial automation, systems such as GraspIt! and Dex-Net 1.0 provide “Grasp Planning as a Service” (GPaaS). This can allow a manufacturer setting up an automated assembly line for a new product to upload part geometry via the Internet to the service and receive a ranked set of robust grasp configurations. As industrial users may be reluctant to share proprietary details of product geometry with any outside parties, this paper proposes a privacy-preserving approach and presents an algorithm where a masked version of the part boundary is uploaded, allowing proprietary aspects of the part geometry to remain confidential. One challenge is the tradeoff between grasp coverage and privacy: balancing the desire for a rich set of alternative grasps based on analysis of graspable surfaces (coverage) against the user’s desire to maximize privacy. We introduce a grasp coverage metric based on dispersion from motion planning, and plot its relationship with privacy (the amount of the object surface that is masked). We implement our algorithm for Dex-Net 1.0 and present case studies of the privacy-coverage tradeoff on a set of 23 industrial parts. Results suggest that masking the part using the convex hull of the proprietary zone can provide grasp coverage with minor distortion to the object similarity metric used to accelerate grasp planning in Dex-Net 1.0. Code, data, and additional information can be found at


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@inproceedings{mahler2016c, title={Privacy-Preserving Cloud-Based Grasp Planning}, author={Mahler, Jeffrey and Hou, Brian and Niyaz, Sherdil and Pokorny, Florian T and Chandra, Ramu and Goldberg, Ken}, booktitle={IEEE CASE}, url={}, year={2016} }