Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train generalist X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. More details can be found on the project website $\href{https://robotics-transformer-x.github.io}{\text{robotics-transformer-x.github.io}}$.

翻译：大规模、高容量模型在多样化数据集上的训练，已在高效处理下游应用方面展现出显著成功。从自然语言处理到计算机视觉等领域，这导致了预训练模型的整合，通用预训练主干成为许多应用的起点。这种整合能否发生在机器人学中？传统上，机器人学习方法为每个应用、每个机器人、甚至每个环境单独训练模型。我们能否训练通用的X-机器人策略，使其能够高效适应新的机器人、任务和环境？本文提供了标准化数据格式的数据集和模型，以探索在机器人操作领域中这一可能性，并附有实验示例，展示了有效的X-机器人策略。我们通过21个机构的合作，从22个不同的机器人收集了数据集，展示了527种技能（160266项任务）。我们表明，在此数据上训练的高容量模型（称为RT-X）展现出正向迁移，并通过利用其他平台的经验提升了多个机器人的能力。更多详情可访问项目网站$\href{https://robotics-transformer-x.github.io}{\text{robotics-transformer-x.github.io}}$。