We propose a novel, object-agnostic method for learning a universal policy for dexterous object grasping from realistic point cloud observations and proprioceptive information under a table-top setting, namely UniDexGrasp++. To address the challenge of learning the vision-based policy across thousands of object instances, we propose Geometry-aware Curriculum Learning (GeoCurriculum) and Geometry-aware iterative Generalist-Specialist Learning (GiGSL) which leverage the geometry feature of the task and significantly improve the generalizability. With our proposed techniques, our final policy shows universal dexterous grasping on thousands of object instances with 85.4% and 78.2% success rate on the train set and test set which outperforms the state-of-the-art baseline UniDexGrasp by 11.7% and 11.3%, respectively.

翻译：我们提出一种新颖的、与物体类别无关的方法，用于在桌面环境下基于真实点云观测与本体感知信息学习通用灵巧抓取策略，即UniDexGrasp++。针对跨数千物体实例学习基于视觉的策略这一挑战，我们提出几何感知课程学习（GeoCurriculum）与几何感知迭代式通才-专才学习（GiGSL），该方法利用任务的几何特征显著提升了泛化能力。通过所提技术，最终策略在数千物体实例上实现通用灵巧抓取，训练集与测试集成功率分别达85.4%与78.2%，较当前最优基线UniDexGrasp分别提升11.7%与11.3%。