一个大脑，任意身体：Gemini Robotics 2 与跨本体

依然处于 GPT-2 时代，原因在于：要达到 GPT-3 的水平，我们首先需要少样本学习能在大量不同任务上真正奏效；其次机器人学至今依然极度受制于环境迁移——如果一个模型只能在你那台具有特定配置的机器人上运行，它能算得上是通用大脑吗？

still GPT-2 and here's why. I think for GPT-3 we need firstly few shot learning to work really well for a lot of different tasks and secondly also with robotics weirdly is still very subject to cross environment, right? If it just works on your robot with your specific setup, is it really a generic brain?

语境：Keerthana 评估具身智能现状，指出机器人模型尚未迈过泛化门槛：不同物理本体和硬件传感器的异构性，使得通用具身大脑远比纯数字 LLM 难以泛化。

支持 → a16z-fei-fei-li-20260904

支持 → the-ezra-jensen-huang-20260923

通过极少量的数据——仅用 200 个示例——你就能让它在许多截然不同的机器人身体上学会大量任务，因为你已经具备了对物理世界进行高度通用理解的基础底座，并懂得如何对其进行控制。

You can with very, very small number of examples, with 200 examples, you can learn a lot of different tasks on a lot of different bodies, because you already have the foundation to do a very generic understanding of the physical world, and also like how to control it

语境：介绍 Gemini Robotics 端侧小模型的少样本跨本体迁移能力：预训练模型赋予物理常识后，仅需几百个演示即可适配新本体。

支持 → sequoia-sutton-20260818