  Learning-based grasp detectors typically assume a precision grasp, where each
finger only has one contact point, and estimate the grasp probability. In this
work, we propose a data generation and learning pipeline that can leverage
power grasping, which has more contact points with an enveloping configuration
and is robust against both positioning error and force disturbance. To train a
grasp detector to prioritize power grasping while still keeping precision
grasping as the secondary choice, we propose to train the network against the
magnitude of disturbance in the gravity direction a grasp can resist
(gravity-rejection score) rather than the binary classification of success. We
also provide an efficient data generation pipeline for a dataset with
gravity-rejection score annotation. In addition to thorough ablation studies,
quantitative evaluation in both simulation and real-robot clarifies the
significant improvement in our approach, especially when the objects are heavy.
