RT-1: Robotics Transformer for Real-World Control at Scale

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 models lies with open-ended task-agnostic training, combined with highcapacity architectures that can absorb all of the…