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