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 https://robotics-transformer-x.github.io.

翻译：在多样化数据集上训练的大容量高性能模型，已在高效处理下游应用任务中展现出显著成功。从自然语言处理到计算机视觉领域，这推动了预训练模型的整合，通用预训练主干网络成为众多应用的起点。这种整合能否在机器人领域实现？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。我们能否训练出能够高效适配新机器人、新任务和新环境的通用X机器人策略？本文提供了标准化数据格式的数据集与模型，旨在探索在机器人操作场景中实现这一可能性的路径，同时提供证明有效X机器人策略的实例实验结果。我们通过21个机构的合作，从22种不同机器人采集数据集，展示了527项技能（160,266个任务）。研究表明，基于该数据训练的高容量模型RT-X展现出正向迁移能力，通过利用其他平台的经验提升了多款机器人的性能。更多详情请访问项目网站 https://robotics-transformer-x.github.io。