GR-3 技術報告

摘要

我們報告了在構建通用機器人策略方面的最新進展，即GR-3的開發。GR-3是一個大規模的視覺-語言-動作（VLA）模型。它展示了在泛化到新物體、環境及涉及抽象概念的指令方面具有卓越能力。此外，它能夠通過最少的人類軌跡數據進行高效微調，從而實現快速且經濟高效地適應新環境。GR-3在處理長期視野和精細任務方面也表現出色，包括需要雙手機器人操作和移動的任務，展現了強健且可靠的性能。這些能力是通過多方面的訓練方案實現的，包括與網絡規模的視覺-語言數據共同訓練、通過VR設備收集的人類軌跡數據進行高效微調，以及利用機器人軌跡數據進行有效的模仿學習。此外，我們還介紹了ByteMini，這是一款多功能雙手機器人，設計上具有卓越的靈活性和可靠性，當與GR-3集成時，能夠完成廣泛的任務。通過大量的現實世界實驗，我們展示了GR-3在多種具有挑戰性的任務上超越了最先進的基線方法pi_0。我們希望GR-3能夠作為邁向構建能夠協助人類日常生活的通用機器人的一步。

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 技術報告

GR-3 Technical Report

摘要

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