  6-DoF grasp pose detection of multi-grasp and multi-object is a challenge
task in the field of intelligent robot. To imitate human reasoning ability for
grasping objects, data driven methods are widely studied. With the introduction
of large-scale datasets, we discover that a single physical metric usually
generates several discrete levels of grasp confidence scores, which cannot
finely distinguish millions of grasp poses and leads to inaccurate prediction
results. In this paper, we propose a hybrid physical metric to solve this
evaluation insufficiency. First, we define a novel metric is based on the
force-closure metric, supplemented by the measurement of the object flatness,
gravity and collision. Second, we leverage this hybrid physical metric to
generate elaborate confidence scores. Third, to learn the new confidence scores
effectively, we design a multi-resolution network called Flatness Gravity
Collision GraspNet (FGC-GraspNet). FGC-GraspNet proposes a multi-resolution
features learning architecture for multiple tasks and introduces a new joint
loss function that enhances the average precision of the grasp detection. The
network evaluation and adequate real robot experiments demonstrate the
effectiveness of our hybrid physical metric and FGC-GraspNet. Our method
achieves 90.5\% success rate in real-world cluttered scenes. Our code is
available at https://github.com/luyh20/FGC-GraspNet.
