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SHIV: Reducing supervisor burden in DAgger using support vectors for efficient learning from demonstrations in high dimensional state spaces., , , , , , и . ICRA, стр. 462-469. IEEE, (2016)GP-GPIS-OPT: Grasp planning with shape uncertainty using Gaussian process implicit surfaces and Sequential Convex Programming., , , , , , и . ICRA, стр. 4919-4926. IEEE, (2015)Industrial Robot Grasping with Deep Learning using a Programmable Logic Controller (PLC)., , , , , , , и . CASE, стр. 97-103. IEEE, (2020)Minimal Work: A Grasp Quality Metric for Deformable Hollow Objects., , , , , и . ICRA, стр. 1546-1552. IEEE, (2020)Comparing human-centric and robot-centric sampling for robot deep learning from demonstrations., , , , , , , и . ICRA, стр. 358-365. IEEE, (2017)REACH: Reducing False Negatives in Robot Grasp Planning with a Robust Efficient Area Contact Hypothesis Model., , , , , и . ISRR, том 20 из Springer Proceedings in Advanced Robotics, стр. 757-772. Springer, (2019)Dex-Net 3.0: Computing Robust Vacuum Suction Grasp Targets in Point Clouds Using a New Analytic Model and Deep Learning., , , , , и . ICRA, стр. 1-8. IEEE, (2018)Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics., , , , , , , и . Robotics: Science and Systems, (2017)Dex-Net 3.0: Computing Robust Robot Suction Grasp Targets in Point Clouds using a New Analytic Model and Deep Learning., , , , , и . CoRR, (2017)Using dVRK teleoperation to facilitate deep learning of automation tasks for an industrial robot., , , , и . CASE, стр. 1-8. IEEE, (2017)