  We focus on the generalization ability of the 6-DoF grasp detection method in
this paper. While learning-based grasp detection methods can predict grasp
poses for unseen objects using the grasp distribution learned from the training
set, they often exhibit a significant performance drop when encountering
objects with diverse shapes and structures. To enhance the grasp detection
methods' generalization ability, we incorporate domain prior knowledge of
robotic grasping, enabling better adaptation to objects with significant shape
and structure differences. More specifically, we employ the physical constraint
regularization during the training phase to guide the model towards predicting
grasps that comply with the physical rule on grasping. For the unstable grasp
poses predicted on novel objects, we design a contact-score joint optimization
using the projection contact map to refine these poses in cluttered scenarios.
Extensive experiments conducted on the GraspNet-1billion benchmark demonstrate
a substantial performance gain on the novel object set and the real-world
grasping experiments also demonstrate the effectiveness of our generalizing
6-DoF grasp detection method.
