Robot learning techniques have the ability to generalize over a wide range of tasks, settings, and objects. Unfortunately, these strategies call for extensive, diverse datasets, which are difficult and costly to obtain in practical robotics contexts. Generalizability in robot learning requires access to priors or data outside the robot’s immediate environment. Data augmentation is a useful tool for enhancing model generalization. But most methods operate in low-level visual space, altering the data in ways like color jitter, Gaussian blurring, and cropping. However, they are still incapable of dealing with significant semantic distinctions in the picture, such as distracting elements, different
This is a companion discussion for the article “Meta AI Introduces GenAug: A New System That Uses Text2Image Models To Enable Robots To Transfer Behaviors Zero-Shot From A Simple Demonstrated Scene To Unseen Scenes of Varying Complexity - https://www.marktechpost.com/2023/02/19/meta-ai-introduces-genaug-a-new-system-that-uses-text2image-models-to-enable-robots-to-transfer-behaviors-zero-shot-from-a-simple-demonstrated-scene-to” submitted on the community's news feed.
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