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Dex-Net 1.0: A cloud-based network of 3D objects for robust grasp planning using a Multi-Armed Bandit model with correlated rewards., , , , , , , , , и . ICRA, стр. 1957-1964. IEEE, (2016)Deep Transfer Learning of Pick Points on Fabric for Robot Bed-Making., , , , , , , , и . ISRR, том 20 из Springer Proceedings in Advanced Robotics, стр. 275-290. Springer, (2019)Disentangling Dense Multi-Cable Knots., , , , , , , , , и . IROS, стр. 3731-3738. IEEE, (2021)Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data., , , , , , , и . ICRA, стр. 9411-9418. IEEE, (2020)Statistical data cleaning for deep learning of automation tasks from demonstrations., , , , , и . CASE, стр. 1142-1149. IEEE, (2017)Comparing Human-Centric and Robot-Centric Sampling for Robot Deep Learning from Demonstrations., , , , , , , и . CoRR, (2016)Robot grasping in clutter: Using a hierarchy of supervisors for learning from demonstrations., , , , , , , и . CASE, стр. 827-834. IEEE, (2016)Planar Robot Casting with Real2Sim2Real Self-Supervised Learning., , , , , , , и . CoRR, (2021)Dynamic regret convergence analysis and an adaptive regularization algorithm for on-policy robot imitation learning., , , , и . Int. J. Robotics Res., (2021)A Dynamic Regret Analysis and Adaptive Regularization Algorithm for On-Policy Robot Imitation Learning., , , , и . WAFR, том 14 из Springer Proceedings in Advanced Robotics, стр. 212-227. Springer, (2018)