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We propose a novel, object-agnostic method for learning a universal policyfor dexterous object grasping from realistic point cloud observations andproprioceptive information under a table-top setting, namely UniDexGrasp++. Toaddress the challenge of learning the vision-based policy across thousands ofobject instances, we propose Geometry-aware Curriculum Learning (GeoCurriculum)and Geometry-aware iterative Generalist-Specialist Learning (GiGSL) whichleverage the geometry feature of the task and significantly improve thegeneralizability. With our proposed techniques, our final policy showsuniversal dexterous grasping on thousands of object instances with 85.4% and78.2% success rate on the train set and test set which outperforms thestate-of-the-art baseline UniDexGrasp by 11.7% and 11.3%, respectively.

UniDexGrasp++: Improving Dexterous Grasping Policy Learning via Geometry-aware Curriculum and Iterative Generalist-Specialist Learning