High-capacity vision-language models (VLMs) are trained on large web datasets, enabling them to effectively recognize visual and language patterns and function across multiple languages. However, for robots to reach a similar level of proficiency, they would need to gather firsthand data across various objects, environments, tasks, and situations. In this context, researchers have introduced Robotic Transformer 2 (RT-2), a vision-language-action (VLA) model that learns from both web and robotics
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