  A challenge in robot grasping is to achieve task-grasping which is to select
a grasp that is advantageous to the success of tasks before and after grasps.
One of the frameworks to address this difficulty is Learning-from-Observation
(LfO), which obtains various hints from human demonstrations. This paper solves
three issues in the grasping skills in the LfO framework: 1) how to
functionally mimic human-demonstrated grasps to robots with limited grasp
capability, 2) how to coordinate grasp skills with reaching body mimicking, 3)
how to robustly perform grasps under object pose and shape uncertainty. A deep
reinforcement learning using contact-web based rewards and domain randomization
of approach directions is proposed to achieve such robust mimicked grasping
skills. Experiment results show that the trained grasping skills can be applied
in an LfO system and executed on a real robot. In addition, it is shown that
the trained skill is robust to errors in the object pose and to the uncertainty
of the object shape and can be combined with various reach-coordination.
