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Hypothesis-based Belief Planning for Dexterous Grasping., , , , , and . CoRR, (2019)Feature-Based Transfer Learning for Robotic Push Manipulation., , and . ICRA, page 1-5. IEEE, (2018)Predicting the unobservable Visual 3D tracking with a probabilistic motion model., , , , , , and . ICRA, page 1849-1855. IEEE, (2011)Task-relevant grasp selection: A joint solution to planning grasps and manipulative motion trajectories., , , , and . IROS, page 907-914. IEEE, (2016)Active vision for dexterous grasping of novel objects., , and . CoRR, (2017)Learning modular and transferable forward models of the motions of push manipulated objects., , , , and . Auton. Robots, 41 (5): 1061-1082 (2017)Dynamic grasp and trajectory planning for moving objects., , , , , , , and . Auton. Robots, 43 (5): 1241-1256 (2019)Analysis of the inertia and dynamics of grasped objects, for choosing optimal grasps to enable torque-efficient post-grasp manipulations., , , , , and . Humanoids, page 171-178. IEEE, (2016)One-shot learning and generation of dexterous grasps for novel objects., , , , , and . Int. J. Robotics Res., 35 (8): 959-976 (2016)Learning better generative models for dexterous, single-view grasping of novel objects., , and . Int. J. Robotics Res., (2019)