OpenAI 和 Figure 机器人背后的技术原理是什么？

作者：多知网发布时间：2024-03-17

OpenAI

OpenAI 和 Figure 机器人背后的技术原理是什么？

以下文章来源于飞哥说AI，作者李志飞高佳

作者 | 高佳

创意 | 李志飞

真正的具身智能提前一年到来？

创建不到两年、估值 26 亿美元的机器人初创公司 Figure，在 X 上亮相它与 OpenAI 的首次合作。全尺寸仿人机器人 Figure 01 正与人类和环境互动，展示它所拥有的识别、计划和执行任务的能力。

Figure 机器人背后的技术原理是什么？OpenAI为其配置的智慧大脑是怎样运作的？与Google发布的机器人系统RT-1、PaLM-E、RT-2是怎样的关系？

以下我们将做以简要解读。

01.

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原理：

Figure类似PaLM-E + RT-1，而非端到端的RT-2

原理上，机器人Figure 看起来类似于Google在2023年3月发布的PaLM-E和RT-1的pipeline组合，而不是Google于2023年7月发布的端到端模型 RT-2。

我们知道，如果机器人要与人进行自然语言交互（比如，人说“把桌子上的苹果拿给主人”），可以大致分为以下两个步骤。

一是机器理解自然语言，并把自然语言转换成机器的抽象计划（所谓 high-level planning），这个抽象的计划可能是一系列的简单自然语言指令（比说“拿起苹果”、“把苹果移到人手上方”、“放开苹果”）。

二是把这个抽象计划转换成底层具体的操控（所谓 low-level 执行），既把简单的自然语言指令转换成一系列的具体动作（比如旋转、移动、抓取、放开等基本动作）。

简单来说，RT-1 仅是一个能听懂简单指令的机械臂，完成上面说的第二个步骤，模型中没有思维链，也不具备推理能力。PaLM-E 让机器人有了聪明的大脑，可以将复杂自然语言指令分解为简单指令，完成上面说的第一个步骤，然后再去调用 RT-1执行动作。

所以，PaLM-E所做的只是自然语言理解以及 Planning 部分的工作，并不涉及机器人动作本身，它是一个VLM（Vision-Language Model ）模型。

RT-2 则是把以上环节端到端地集成到了一起，它能够用复杂文本指令直接操控机械臂，中间不再需要将其转化成简单指令，通过自然语言就可得到最终的 Action，它是一个VLA（Vision-Language-Action Model）模型。

端到端的好处，是通用、能自动适应环境的各种变化，但问题是决策速度慢，很难做到Figure这种200hz的决策速度，比如RT-2论文里提到的决策频率是1到5hz，具体取决于语言模型的参数规模。

关于RT-1、PaLM-E、RT-2的来世今生、区别和联系可以参见之前关于RT-2的文章：

02.

亮点：既能用到大语言模型的常识和COT，又能达到人一般的操控速度

Figure 视频的亮点是它既利用了大语言模型的常识和思维链COT的能力，又实现了快速的底层操控，几乎接近人的速度。

比如在视频中，当人说“我饿了”，Figure思考了2-3秒后，小心翼翼地伸手抓住苹果，并迅速给人递过来。

因为Figure基于大语言模型的常识，明白苹果是它面前唯一可以“吃”的事物，在人类没有任何提示和说明的前提下，即可以接近于人类的反应速度，与人自然交互。

另外，也用上了大语言模型的长上下文的理解能力，比如“你能把它们放到那里吗”，谁是“它们”，“那里”是哪里？这些只有大语言模型才有能力精准抓取长上下文里的指代关系。

03.

Figure 是否用到了Sora?

最近，OpenAI的Sora红了半边天，而Sora自称是世界的模拟器，所以很多人自然会疑问这个机器人是否用到了Sora。

肯定的是，Figure与Sora一点关系都没有，因为Sora现阶段主要是生成，不是理解，就算未来Sora既能理解也能生成，是否能端到端做到200hz的决策速度也是一个很大的问题。

04.

Figure 是否用到了

远程操控

有人觉得视频是远程操控（所谓Teleop）录制的，但Figure创始人Brett Adcock强调了视频不是Teleop，录制速度也是1倍原速度，所以视频中能看到回答问题的明显延迟（因为语音识别、大语言模型、TTS是通过pipeline方式连起来的，都需要计算时间）。

05.

Figure 是端到端模型吗？

Figure创始人Brett Adcock上面这个Post提到是端到端的神经网络，个人觉得这可能是口误吧。从他们技术负责人的Twitter post里可以看出，至少用了两个神经网络模型，一是OpenAI的GPT4V（类似于Google的PaLM-E）；另外一个是机器人操控的模型（类似于RT-1）。

所以Figure不是类似于RT-2的“端到端”模型，而是一个pipeline系统。

结语

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总结一下，我们可以理解为Figure的机器人模型是：

GPT4V + 操控模型～= Google的PaLM-E + RT-1

再次感叹，OpenAI的“远见卓识”，在机器人与大模型结合的领域，又让隔壁的Google起了大早，赶了晚集。一如既往，OpenAI超越Google的方式不是在技术原理，而是在于产品定义以及宣传方式。比如，与RT-2用一个机械臂演示不一样，他们用了一个真实的人形机器人来演示。

此外，他们通过展示机器人的操控速度和自然度来吸引观众的眼球。这些都比Google那种纯工程师演示的方式倍加有吸引力。作为观者，我们一方面惋惜于Google，同时也乐于再次见证这类军备竞赛的上演，Google加油！

Figure联合创始人兼首席执行官Bred Adcock表示，“我们的目标是训练一个世界模型，以操作十亿单位级别的仿人机器人。”这些机器人，可以消除对不安全和不理想工作的需求，最终让人类拥有更有意义的生活，这也与OpenAI的「超级对齐」愿景不谋而合。

计算机是虚拟世界的通用平台，机器人是物理世界的通用平台。

从特斯拉的Optimus到今天的Figure，未来的模型能力和机器人硬件会如何平衡，从广告噱头到量产应用还有多远？人形机器人百家争鸣的春秋时代已拉开序幕。

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#07c160;--weui-BLUE: #10aeff;--weui-INDIGO: #1196ff;--weui-PURPLE: #8183ff;--weui-WHITE: rgba(255, 255, 255, .8);--weui-LINK: #7d90a9;--weui-TEXTGREEN: #259c5c;--weui-FG: #fff;--weui-BG: #000;--weui-TAG-TEXT-RED: rgba(250, 81, 81, .6);--weui-TAG-BACKGROUND-RED: rgba(250, 81, 81, .1);--weui-TAG-TEXT-ORANGE: rgba(250, 157, 59, .6);--weui-TAG-BACKGROUND-ORANGE: rgba(250, 157, 59, .1);--weui-TAG-TEXT-GREEN: rgba(6, 174, 86, .6);--weui-TAG-BACKGROUND-GREEN: rgba(6, 174, 86, .1);--weui-TAG-TEXT-BLUE: rgba(16, 174, 255, .6);--weui-TAG-BACKGROUND-BLUE: rgba(16, 174, 255, .1);--weui-TAG-TEXT-BLACK: rgba(255, 255, 255, .5);--weui-TAG-BACKGROUND-BLACK: rgba(255, 255, 255, .05)}}@media screen and (min-width:1024px){body:not(.pages_skin_pc) .wx-root[data-weui-theme=dark],body:not(.pages_skin_pc) body[data-weui-theme=dark]{--appmsgExtra-BG: #121212}}@media screen and (min-width:1024px){body:not(.pages_skin_pc)body:not(.pages_skin_pc){background:var(--weui-BG-2)}}@media screen and (min-width:1024px) and (prefers-color-scheme:dark){body:not(.pages_skin_pc) .wx-root[data-weui-mode=care]:not([data-weui-theme=light]),body:not(.pages_skin_pc) body[data-weui-mode=care]:not([data-weui-theme=light]){--weui-BG-0: #111;--weui-BG-1: #1e1e1e;--weui-BG-2: #191919;--weui-BG-3: #202020;--weui-BG-4: #404040;--weui-BG-5: #2c2c2c;--weui-FG-0: rgba(255, 255, 255, .85);--weui-FG-HALF: rgba(255, 255, 255, .65);--weui-FG-1: rgba(255, 255, 255, .55);--weui-FG-2: rgba(255, 255, 255, .35);--weui-FG-3: rgba(255, 255, 255, .1);--weui-FG-4: rgba(255, 255, 255, .15);--weui-FG-5: rgba(255, 255, 255, .1);--weui-RED: #fa5151;--weui-ORANGERED: #ff6146;--weui-ORANGE: #c87d2f;--weui-YELLOW: #cc9c00;--weui-GREEN: #74a800;--weui-LIGHTGREEN: #3eb575;--weui-BRAND: #07c160;--weui-BLUE: #10aeff;--weui-INDIGO: #1196ff;--weui-PURPLE: #8183ff;--weui-WHITE: rgba(255, 255, 255, .8);--weui-LINK: #7d90a9;--weui-TEXTGREEN: #259c5c;--weui-FG: #fff;--weui-BG: #000;--weui-TAG-TEXT-RED: rgba(250, 81, 81, .6);--weui-TAG-BACKGROUND-RED: rgba(250, 81, 81, .1);--weui-TAG-TEXT-ORANGE: rgba(250, 157, 59, .6);--weui-TAG-BACKGROUND-ORANGE: rgba(250, 157, 59, .1);--weui-TAG-TEXT-GREEN: rgba(6, 174, 86, .6);--weui-TAG-BACKGROUND-GREEN: rgba(6, 174, 86, .1);--weui-TAG-TEXT-BLUE: rgba(16, 174, 255, .6);--weui-TAG-BACKGROUND-BLUE: rgba(16, 174, 255, .1);--weui-TAG-TEXT-BLACK: rgba(255, 255, 255, .5);--weui-TAG-BACKGROUND-BLACK: rgba(255, 255, 255, .05)}}@media screen and (min-width:1024px) and (prefers-color-scheme:dark){body:not(.pages_skin_pc) .wx-root:not([data-weui-theme=light]),body:not(.pages_skin_pc) body:not([data-weui-theme=light]){--weui-BG-0: #111;--weui-BG-1: #1e1e1e;--weui-BG-2: #191919;--weui-BG-3: #202020;--weui-BG-4: #404040;--weui-BG-5: #2c2c2c;--weui-FG-0: rgba(255, 255, 255, .8);--weui-FG-HALF: rgba(255, 255, 255, .6);--weui-FG-1: rgba(255, 255, 255, .5);--weui-FG-2: rgba(255, 255, 255, .3);--weui-FG-3: rgba(255, 255, 255, .1);--weui-FG-4: rgba(255, 255, 255, .15);--weui-FG-5: rgba(255, 255, 255, .1);--weui-RED: #fa5151;--weui-ORANGERED: #ff6146;--weui-ORANGE: #c87d2f;--weui-YELLOW: #cc9c00;--weui-GREEN: #74a800;--weui-LIGHTGREEN: #3eb575;--weui-BRAND: #07c160;--weui-BLUE: #10aeff;--weui-INDIGO: #1196ff;--weui-PURPLE: #8183ff;--weui-WHITE: rgba(255, 255, 255, .8);--weui-LINK: #7d90a9;--weui-TEXTGREEN: #259c5c;--weui-FG: #fff;--weui-BG: #000;--weui-TAG-TEXT-RED: rgba(250, 81, 81, .6);--weui-TAG-BACKGROUND-RED: rgba(250, 81, 81, .1);--weui-TAG-TEXT-ORANGE: rgba(250, 157, 59, .6);--weui-TAG-BACKGROUND-ORANGE: rgba(250, 157, 59, .1);--weui-TAG-TEXT-GREEN: rgba(6, 174, 86, .6);--weui-TAG-BACKGROUND-GREEN: rgba(6, 174, 86, .1);--weui-TAG-TEXT-BLUE: rgba(16, 174, 255, .6);--weui-TAG-BACKGROUND-BLUE: rgba(16, 174, 255, .1);--weui-TAG-TEXT-BLACK: rgba(255, 255, 255, .5);--weui-TAG-BACKGROUND-BLACK: rgba(255, 255, 255, .05)}}@media screen and (min-width:1024px) and (prefers-color-scheme:dark){body:not(.pages_skin_pc) 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