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We address a class of manipulation problems where the robot perceives thescene with a depth sensor and can move its end effector in a space with sixdegrees of freedom -- 3D position and orientation. Our approach is to formulatethe problem as a Markov decision process (MDP) with abstract yet generallyapplicable state and action representations. Finding a good solution to the MDPrequires adding constraints on the allowed actions. We develop a specific setof constraints called hierarchical $\text{SE}(3)$ sampling (HSE3S) which causesthe robot to learn a sequence of gazes to focus attention on the task-relevantparts of the scene. We demonstrate the effectiveness of our approach on threechallenging pick-place tasks (with novel objects in clutter and nontrivialplaces) both in simulation and on a real robot, even though all training isdone in simulation.

Learning 6-DoF Grasping and Pick-Place Using Attention Focus