  In this paper, we tackle the problem of grasping transparent and specular
objects. This issue holds importance, yet it remains unsolved within the field
of robotics due to failure of recover their accurate geometry by depth cameras.
For the first time, we propose ASGrasp, a 6-DoF grasp detection network that
uses an RGB-D active stereo camera. ASGrasp utilizes a two-layer learning-based
stereo network for the purpose of transparent object reconstruction, enabling
material-agnostic object grasping in cluttered environments. In contrast to
existing RGB-D based grasp detection methods, which heavily depend on depth
restoration networks and the quality of depth maps generated by depth cameras,
our system distinguishes itself by its ability to directly utilize raw IR and
RGB images for transparent object geometry reconstruction. We create an
extensive synthetic dataset through domain randomization, which is based on
GraspNet-1Billion. Our experiments demonstrate that ASGrasp can achieve over
90% success rate for generalizable transparent object grasping in both
simulation and the real via seamless sim-to-real transfer. Our method
significantly outperforms SOTA networks and even surpasses the performance
upper bound set by perfect visible point cloud inputs.Project page:
https://pku-epic.github.io/ASGrasp
