  In robotic grasping, objects are often occluded in ungraspable configurations
such that no pregrasp pose can be found, eg large flat boxes on the table that
can only be grasped from the side. Inspired by humans' bimanual manipulation,
eg one hand to lift up things and the other to grasp, we address this type of
problems by introducing pregrasp manipulation - push and lift actions. We
propose a model-free Deep Reinforcement Learning framework to train control
policies that utilize visual information and proprioceptive states of the robot
to autonomously discover robust pregrasp manipulation. The robot arm learns to
first push the object towards a support surface and establishes a pivot to lift
up one side of the object, thus creating a clearance between the object and the
table for possible grasping solutions. Furthermore, we show the effectiveness
of our proposed learning framework in training robust pregrasp policies that
can directly transfer from simulation to real hardware through suitable design
of training procedures, state, and action space. Lastly, we evaluate the
effectiveness and the generalisation ability of the learned policies in
real-world experiments, and demonstrate pregrasp manipulation of objects with
various size, shape, weight, and surface friction.
