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Dexterous robotic hands have the capability to interact with a wide varietyof household objects to perform tasks like grasping. However, learning robustreal world grasping policies for arbitrary objects has proven challenging dueto the difficulty of generating high quality training data. In this work, wepropose a learning system (ISAGrasp) for leveraging a small number of humandemonstrations to bootstrap the generation of a much larger dataset containingsuccessful grasps on a variety of novel objects. Our key insight is to use acorrespondence-aware implicit generative model to deform object meshes anddemonstrated human grasps in order to generate a diverse dataset of novelobjects and successful grasps for supervised learning, while maintainingsemantic realism. We use this dataset to train a robust grasping policy insimulation which can be deployed in the real world. We demonstrate graspingperformance with a four-fingered Allegro hand in both simulation and the realworld, and show this method can handle entirely new semantic classes andachieve a 79% success rate on grasping unseen objects in the real world.

Learning Robust Real-World Dexterous Grasping Policies via Implicit Shape Augmentation