  Daily objects embedded in a contextual environment are often ungraspable
initially. Whether it is a book sandwiched by other books on a fully packed
bookshelf or a piece of paper lying flat on the desk, a series of nonprehensile
pregrasp maneuvers is required to manipulate the object into a graspable state.
Humans are proficient at utilizing environmental contacts to achieve
manipulation tasks that are otherwise impossible, but synthesizing such
nonprehensile pregrasp behaviors is challenging to existing methods. We present
a novel method that combines graph search, optimal control, and a
learning-based objective function to synthesize physically realistic and
diverse nonprehensile pre-grasp motions that leverage the external contacts.
Since the ``graspability'' of an object in context with its surrounding is
difficult to define, we utilize a dataset of dexterous grasps to learn a metric
which implicitly takes into account the exposed surface of the object and the
finger tip locations. Our method can efficiently discover hand and object
trajectories that are certified to be physically feasible by the simulation and
kinematically achievable by the dexterous hand. We evaluate our method on eight
challenging scenarios where nonprehensile pre-grasps are required to succeed.
We also show that our method can be applied to unseen objects different from
those in the training dataset. Finally, we report quantitative analyses on
generalization and robustness of our method, as well as an ablation study.
