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：我们提出了一种新颖的、与对象无关的方法，用于在桌面场景下基于真实点云观测和本体感知信息学习通用灵巧抓取策略，即UniDexGrasp++。为解决跨数千个对象实例学习视觉基础策略的挑战，我们提出了几何感知课程学习（GeoCurriculum）和几何感知迭代通才-专才学习（GiGSL），二者利用任务的几何特征显著提升了泛化能力。通过提出的技术，最终策略在数千个对象实例上实现了通用灵巧抓取，在训练集和测试集上的成功率分别达到85.4%和78.2%，比当前最优基线UniDexGrasp高出11.7%和11.3%。