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.

翻译：大规模、高容量模型在多样化数据集上训练后，在高效处理下游应用方面展现出显著成功。在从NLP到计算机视觉的领域中，这导致了预训练模型的整合，通用预训练骨干网络成为许多应用的起点。这种整合能否在机器人领域实现？传统上，机器人学习方法会为每个应用、每个机器人甚至每个环境分别训练模型。我们能否训练出能够高效适应新机器人、新任务和新环境的通用型X-机器人策略？本文提供了标准化数据格式的数据集和模型，使得在机器人操作背景下探索这种可能性成为可能，同时给出了有效X-机器人策略的示例实验结果。我们通过21个机构的合作，从22种不同机器人收集了数据集，展示了527项技能（160266个任务）。研究表明，在此数据上训练的高容量模型（我们称之为RT-X）展现出正迁移，并通过利用其他平台的经验提升了多种机器人的能力。