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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)

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Robot grasping in clutter: Using a hierarchy of supervisors for learning from demonstrations., , , , , , , and . CASE, page 827-834. IEEE, (2016)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)Dex-Net MM: Deep Grasping for Surface Decluttering with a Low-Precision Mobile Manipulator., , , , , , , , and . CASE, page 1373-1379. IEEE, (2019)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 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)Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data., , , , , , , and . ICRA, page 9411-9418. IEEE, (2020)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)Statistical data cleaning for deep learning of automation tasks from demonstrations., , , , , and . CASE, page 1142-1149. IEEE, (2017)Untangling Dense Knots by Learning Task-Relevant Keypoints., , , , , , , , , and . CoRR, (2020)