Name
Abstract | ||
|---|---|---|
Learning-based approaches to robust robot grasp planning can grasp a wide variety of objects, but may be prone to failure on some objects. Inspired by recent results in computer vision, we define a class of “adversarial grasp objects that are physically similar to a given object but significantly less ”graspable” in terms of a specified robot grasping policy. We present three algorithms for synthesizing adversarial grasp objects under the grasp reliability measure of Dex-Net 1.0 for parallel-jaw grippers: 1) two analytic algorithms that perturb vertices on antipodal faces (one that uses random perturbations and one that uses systematic perturbations), and 2) a deep-learning-based approach using a variation of the Cross-Entropy Method (CEM) augmented with a generative adversarial network (GAN) to synthesize classes of adversarial grasp objects represented by discrete Signed Distance Functions. The random perturbation algorithm reduces graspability by 32%, 12%, and 32% for intersected cylinders, intersected prisms, and ShapeNet bottles, respectively, while maintaining shape similarity using geometric constraints. The systematic perturbation algorithm reduces graspability by 32%, 11%, and 21%; and the GAN reduces graspability by 22%, 36%, and 17%, on the same objects. We use the algorithms to generate and 3D print adversarial grasp objects. Simulation and physical experiments confirm that all algorithms are effective at reducing graspability. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/COASE.2019.8843059 | 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE) |
Keywords | DocType | ISSN |
robust robot grasp planning,computer vision,Dex-Net 1.0,parallel-jaw grippers,deep-learning-based approach,cross-entropy method,generative adversarial network,random perturbation algorithm,discrete signed distance functions,3D print adversarial grasp objects,specified robot grasping policy | Conference | 2161-8070 |
ISBN | Citations | PageRank |
978-1-7281-0357-0 | 1 | 0.35 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Wang | 1 | 1 | 0.35 |
| David Tseng | 2 | 1 | 0.35 |
| Pusong Li | 3 | 1 | 0.35 |
| Yiding Jiang | 4 | 3 | 2.41 |
| Menglong Guo | 5 | 12 | 2.11 |
| Michael Danielczuk | 6 | 29 | 8.34 |
| Jeffrey Mahler | 7 | 98 | 11.06 |
| Jeffrey Ichnowski | 8 | 7 | 9.65 |
| Ken Goldberg | 9 | 3785 | 369.80 |