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GP-GPIS-OPT: Grasp planning with shape uncertainty using Gaussian process implicit surfaces and Sequential Convex Programming., , , , , , and . ICRA, page 4919-4926. IEEE, (2015)SHIV: Reducing supervisor burden in DAgger using support vectors for efficient learning from demonstrations in high dimensional state spaces., , , , , , and . ICRA, page 462-469. IEEE, (2016)Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Point Clouds., , , , , , and . CoRR, (2018)Minimal Work: A Grasp Quality Metric for Deformable Hollow Objects., , , , , and . ICRA, page 1546-1552. IEEE, (2020)Industrial Robot Grasping with Deep Learning using a Programmable Logic Controller (PLC)., , , , , , , and . CASE, page 97-103. IEEE, (2020)Multi-armed bandit models for 2D grasp planning with uncertainty., , , , , , , , and . CASE, page 572-579. IEEE, (2015)Privacy-preserving Grasp Planning in the Cloud., , , , , and . CASE, page 468-475. IEEE, (2016)Large-scale supervised learning of the grasp robustness of surface patch pairs., , , , , , and . SIMPAR, page 216-223. IEEE, (2016)Iterative Noise Injection for Scalable Imitation Learning., , , , , , and . CoRR, (2017)On-Policy Dataset Synthesis for Learning Robot Grasping Policies Using Fully Convolutional Deep Networks., , and . IEEE Robotics Autom. Lett., 4 (2): 1357-1364 (2019)