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.

本文关注于 6 自由度抓取检测方法的泛化能力。通过引入机器人抓握的领域先验知识，通过物理约束正则化和接触得分联合优化，提升了模型的泛化能力，并在实验中证明了该方法的有效性。