  Robust task-oriented grasp planning is vital for autonomous robotic precision
assembly tasks. Knowledge of the objects' geometry and preconditions of the
target task should be incorporated when determining the proper grasp to
execute. However, several factors contribute to the challenges of realizing
these grasps such as noise when controlling the robot, unknown object
properties, and difficulties modeling complex object-object interactions. We
propose a method that decomposes this problem and optimizes for grasp
robustness, precision, and task performance by learning three cascaded
networks. We evaluate our method in simulation on three common assembly tasks:
inserting gears onto pegs, aligning brackets into corners, and inserting shapes
into slots. Our policies are trained using a curriculum based on large-scale
self-supervised grasp simulations with procedurally generated objects. Finally,
we evaluate the performance of the first two tasks with a real robot where our
method achieves 4.28mm error for bracket insertion and 1.44mm error for gear
insertion.
