What is the impact of dataset diversity (e.g., number of tasks, domain variability) on the zero-shot generaliz

Abstract

Abstract: By transferring knowledge from large, diverse, taskagnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small taskspecific datasets to a high level of performance.While this capability has been demonstrated in other fields such as computer vision, natural language processing or speech recognition, it remains to be shown in robotics, where the generalization capabilities of the models are particularly critical due to the difficulty of collecting real-world robotic data.We argue that one of the keys to the success of such general robotic

Research Question

What is the impact of dataset diversity (e.g., number of tasks, domain variability) on the zero-shot generalization performance of RT-1 compared to contrastive models, evaluated using SPL and success rate on unseen tasks from the MetaWorld benchmark?

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