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

翻译：本文解决了透明与镜面物体的抓取问题。该问题在机器人领域具有重要意义，但由于深度相机无法恢复此类物体的精确几何结构而长期未获解决。我们首次提出ASGrasp——一种基于RGB-D主动立体相机的六自由度抓取检测网络。ASGrasp采用双层学习型立体网络实现透明物体重建，从而在杂乱环境中实现材质无关的物体抓取。与现有依赖深度修复网络及深度相机生成地图质量的RGB-D抓取检测方法不同，本系统凭借直接利用原始红外与RGB图像进行透明物体几何重建的能力脱颖而出。我们通过域随机化技术构建了基于GraspNet-1Billion的大规模合成数据集。实验表明，ASGrasp在仿真与真实环境中均可通过无缝的仿真-现实迁移实现超过90%的可泛化透明物体抓取成功率。该方法显著优于最先进网络，甚至超越了完美可见点云输入所设定的性能上限。项目主页：https://pku-epic.github.io/ASGrasp