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Robot grasping in clutter: Using a hierarchy of supervisors for learning from demonstrations., , , , , , , and . CASE, page 827-834. IEEE, (2016)Comparing Human-Centric and Robot-Centric Sampling for Robot Deep Learning from Demonstrations., , , , , , , and . CoRR, (2016)Planar Robot Casting with Real2Sim2Real Self-Supervised Learning., , , , , , , and . CoRR, (2021)Dynamic regret convergence analysis and an adaptive regularization algorithm for on-policy robot imitation learning., , , , and . Int. J. Robotics Res., (2021)A Dynamic Regret Analysis and Adaptive Regularization Algorithm for On-Policy Robot Imitation Learning., , , , and . WAFR, volume 14 of Springer Proceedings in Advanced Robotics, page 212-227. Springer, (2018)Learning accurate kinematic control of cable-driven surgical robots using data cleaning and Gaussian Process Regression., , , , , , , , , and 1 other author(s). CASE, page 532-539. IEEE, (2014)Dex-Net MM: Deep Grasping for Surface Decluttering with a Low-Precision Mobile Manipulator., , , , , , , , and . CASE, page 1373-1379. IEEE, (2019)Dex-Net 1.0: A cloud-based network of 3D objects for robust grasp planning using a Multi-Armed Bandit model with correlated rewards., , , , , , , , , and . ICRA, page 1957-1964. IEEE, (2016)Deep Transfer Learning of Pick Points on Fabric for Robot Bed-Making., , , , , , , , and . ISRR, volume 20 of Springer Proceedings in Advanced Robotics, page 275-290. Springer, (2019)Disentangling Dense Multi-Cable Knots., , , , , , , , , and . IROS, page 3731-3738. IEEE, (2021)