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It supports flexible task and observation definitions and can be quickly finetuned to new observation and action spaces. Octo模型由Transformer架构驱动，经过在大规模多机器人操控数据集上的预训练，支持多任务、多传感器输入和多动作空间，能够高效微调并适应新领域的机器人任务。 Octo uses a modular attention structure in its transformer backbone, allowing it to be effectively finetuned to robot setups with new sensory inputs, action spaces, and morphologies, using only a small target domain dataset and accessible compute budgets.

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Octo 模型，开源通才机器人策略，可以通过微调适应新的机器人和任务（包括新的感官输入和动作空间）。 Octo 模型的设计强调灵活性和规模：它支持各种常用的机器人、传感器配置和动作，同时提供可在大量数据上进行训练的通用且可扩展的配方。 Octo 能够对新领域进行数据高效的微调，并且优于从头开始训练以及最先进的预训练视觉表示。 每个领域使用∼100目标演示和相同的微调超参数。 Octo模型的 设计强调灵活性和可扩展性，旨在支持各种常用的机器人、传感器配置和动作，同时提供一个通用且可扩展的训练方法，可以在大量数据上进行训练。 Octo 支持自然语言指令和目标图像、观察历史以及通过扩散解码实现的多模态动作分布。

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