GR-3技术报告

摘要

我们报告了在构建通用机器人策略方面的最新进展，即GR-3的开发。GR-3是一个大规模视觉-语言-动作（VLA）模型，展现了在适应新物体、环境及涉及抽象概念的指令方面卓越的泛化能力。此外，它能够通过少量人类轨迹数据高效微调，实现快速且经济地向新场景的迁移。GR-3在处理长期视野和精细操作任务上同样表现出色，包括需要双手协作和移动操作的任务，展现了其稳健可靠的性能。这些能力得益于多方面的训练策略，包括与网络规模视觉语言数据的协同训练、基于VR设备收集的人类轨迹数据的高效微调，以及利用机器人轨迹数据进行有效的模仿学习。此外，我们推出了ByteMini，一款设计灵活可靠、功能多样的双手移动机器人，与GR-3结合后能够完成广泛的任务。通过大量实际实验，我们证明GR-3在多种挑战性任务上超越了当前最先进的基线方法pi_0。我们希望GR-3能成为迈向构建能够辅助人类日常生活的通用机器人道路上的一步。

English

We report our recent progress towards building generalist robot policies, the development of GR-3. GR-3 is a large-scale vision-language-action (VLA) model. It showcases exceptional capabilities in generalizing to novel objects, environments, and instructions involving abstract concepts. Furthermore, it can be efficiently fine-tuned with minimal human trajectory data, enabling rapid and cost-effective adaptation to new settings. GR-3 also excels in handling long-horizon and dexterous tasks, including those requiring bi-manual manipulation and mobile movement, showcasing robust and reliable performance. These capabilities are achieved through a multi-faceted training recipe that includes co-training with web-scale vision-language data, efficient fine-tuning from human trajectory data collected via VR devices, and effective imitation learning with robot trajectory data. In addition, we introduce ByteMini, a versatile bi-manual mobile robot designed with exceptional flexibility and reliability, capable of accomplishing a wide range of tasks when integrated with GR-3. Through extensive real-world experiments, we show GR-3 surpasses the state-of-the-art baseline method, pi_0, on a wide variety of challenging tasks. We hope GR-3 can serve as a step towards building generalist robots capable of assisting humans in daily life.

GR-3技术报告

GR-3 Technical Report

摘要

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