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High-capacity vision-language models (VLMs) are trained on web-scale datasets, making these systems remarkably good at recognising visual or language patterns and operating across different languages. But for robots to achieve a similar level of competency, they would need to collect robot data, first-hand, across every object, environment, task, and situation. Here, researchers have introduced Robotic Transformer 2 (RT-2), a novel vision-language-action (VLA) model that learns from both web and
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