RT-1：面向大规模真实世界控制的机器人Transformer
RT-1: Robotics Transformer for Real-World Control at Scale
AI总结：
本文提出Robotics Transformer模型，通过任务无关的大规模训练和高容量架构，在真实机器人数据上验证了数据规模、模型规模与多样性对泛化能力的关键作用。
摘要：
通过从大规模、多样化、任务无关的数据集中迁移知识，现代机器学习模型能够以零样本方式或借助小型任务特定数据集，高水平地解决特定的下游任务。虽然这一能力已在计算机视觉、自然语言处理或语音识别等领域得到验证，但在机器人领域仍有待展示，因为收集真实世界机器人数据的难度使得模型的泛化能力尤为关键。我们认为，此类通用机器人模型成功的关键之一在于开放式、任务无关的训练，并结合能够吸收所有多样化机器人数据的高容量架构。在本文中，我们提出了一类模型，称为Robotics Transformer，该模型展现出有前景的可扩展模型特性。我们基于对执行真实世界任务的真实机器人的大规模数据收集，研究不同模型类别及其泛化能力随数据规模、模型规模和数据多样性的变化，从而验证了我们的结论。项目网站和视频可在robotics-transformer1.github.io找到。
英文摘要：
By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specific 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 high-capacity architectures that can absorb all of the diverse, robotic data. In this paper, we present a model class, dubbed Robotics Transformer, that exhibits promising scalable model properties. We verify our conclusions in a study of different model classes and their ability to generalize as a function of the data size, model size, and data diversity based on a large-scale data collection on real robots performing real-world tasks. The project's website and videos can be found at robotics-transformer1.github.io
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
