Robust Task-Based Grasping as a Service

Jingyi Song, Ajay Tanwani, Jeffrey Ichnowski, Michael Danielczuk, Kate Sanders, Jackson Chui, Juan A Ojea, Ken Goldberg

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), 2020.

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Abstract

Robot grasping for automation must be robust to the inherent uncertainty in perception, control, and physical properties such as friction. Computing robust grasp points on a given object is even more challenging when there are constraints due to a task intended to be performed with the object, for example in assembly, packing, and/or tool use. To compute grasps that robustly achieve task requirements, we designed an intuitive user interface that takes an object mesh as input and displays it, allowing non-specialists to indicate “stay-out” zones by painting facets of the mesh and to indicate desired forces and torques by drawing vectors. The interface then sends this specification to our server which computes resulting grasps and send them back to the client where the resulting parallel-jaw grasp axes are displayed color-coded by robustness. We implemented this interface in the cloud-based “Dex-Net as a Service-Task (DNaaS-Task)” system that runs on any browser and reports examples. The system is available at: https://dex-net.app.