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Robot learning methods have the potential for widespread generalizationacross tasks, environments, and objects. However, these methods require largediverse datasets that are expensive to collect in real-world robotics settings.For robot learning to generalize, we must be able to leverage sources of dataor priors beyond the robot's own experience. In this work, we posit thatimage-text generative models, which are pre-trained on large corpora ofweb-scraped data, can serve as such a data source. We show that despite thesegenerative models being trained on largely non-robotics data, they can serve aseffective ways to impart priors into the process of robot learning in a waythat enables widespread generalization. In particular, we show how pre-trainedgenerative models can serve as effective tools for semantically meaningful dataaugmentation. By leveraging these pre-trained models for generating appropriate"semantic" data augmentations, we propose a system GenAug that is able tosignificantly improve policy generalization. We apply GenAug to tabletopmanipulation tasks, showing the ability to re-target behavior to novelscenarios, while only requiring marginal amounts of real-world data. Wedemonstrate the efficacy of this system on a number of object manipulationproblems in the real world, showing a 40% improvement in generalization tonovel scenes and objects.

GenAug: Retargeting behaviors to unseen situations via Generative Augmentation