iPhone 包装背后的机器人机会：Tutor Intelligence 的全栈路径

简要描述Brief Description

iPhone包装所揭示的机器人领域机遇What iPhone Packaging Reveals About the Robotics Opportunity

目录Table of Contents

• 简介
  Intro
• 导师智能一览
  Tutor Intelligence at a glance
• 从工作本身入手，而不是从模型入手
  Start with the job, not the model
• 机器人技术：终极系统工程问题
  Robotics as the ultimate systems problem
• 为什么叫作 Tutor，以及 Tutor 是否会消失
  Why it’s called Tutor, and whether tutors go away
• 前沿模型如何加速机器人技术发展
  How frontier models are accelerating robotics
• 自主研发机器人：Cassie 和 Sonny
  Building robots in-house: Cassie and Sonny
• 向首次购买机器人的客户销售
  Selling to first-time robot buyers
• 为什么选择中小型企业
  Why small and mid-sized businesses
• 工厂、中国与美国供应链
  Factories, China and the US supply chain
• 大规模真实世界强化学习
  Real-world reinforcement learning at scale

简介Intro

无论别人怎么说，如今是2026年，我们拥有一条高度互联、相互依存的全球供应链。美国公司和中国公司都在认真思考，这种格局的长期战略会是什么样。归根结底，大家都在努力建设自己的工业产能。No matter what anybody says, today in 2026, we have a highly interconnected, interdependent global supply chain. Both US companies and Chinese companies are thinking very hard about what does the long-term strategy of this look like? And ultimately, everybody trying to build their industrial capacity.

我们听过很多关于机器人的推介，但很少有人会思考这一切最终要让机器人做什么。我们卖机器人给的客户，几乎都从未买过机器人。We see a lot of robot pitches. Very few people thinking about what the robot is supposed to do at the end of all of this. Almost everybody we're selling a robot to has never purchased a robot before.

市场上几乎所有机会都是从零开始的。现实情况是，你需要构建一个全栈平台。Tutor Robots 已帮助制造并向美国消费者运送了超过 2.5 亿件产品。Almost all of the market is Greenfield. The reality is you need to build a full stack platform. Tutor Robots have helped manufacture and ship over a quarter of a billion products to American consumers.

我们正在训练前沿机器人 AI 模型、大规模制造机器人，并将其直接部署到供应链中规模最大的企业。机器人领域的产品创新是如何进行的？大多数公司还停留在 V1 阶段，而你们已经推出了两个版本的机器人，并且持续快速迭代。We're training frontier robot AI models, manufacturing robots at scale and directly deploying into the largest businesses in the supply chain. How does product innovation work in robotics? Most companies are just on V1 whereas you're got two versions of the robot and you continue to iterate really fast.

导师智能一览Tutor Intelligence at a glance

那么，Josh，感谢你，呃，感谢你加入我们。我们请到了 Josh Gruinstein，他是 Tutor Intelligence 的首席执行官、创始人兼联合创始人。今天我们要聊聊机器人。开始之前，也许你可以先简单介绍一下自己和公司。So Josh, thanks for uh for joining us. So, uh, we're with Josh Gruinstein, who is the CEO and founder, co-founder of of, um, Tutor Intelligence, and we're going to be talking about robots today. And so, maybe before we jump in, just give a quick background maybe of yourself and then the company.

是的。我叫 Josh。大约五年半前，我创办了 Tutor，当时的核心理念是：只要能开始把机器人部署到现实世界中，也就是物理经济里最需要机器人的各个地方，就能逐步构建起所需的数据集、模型和供应链，让机器人遍布整个物理经济。Yeah. So, I'm Josh. I started tutor about five and a half years ago uh with the core thesis that if you could just start deploying robots out into the world into all the places in the physical economy where robots are needed the most, you could start to build the data sets, the models, and the supply chain that's needed to proliferate robots uh throughout the physical economy.

所以我们大约有 160 人，主要位于波士顿。我们训练前沿机器人 AI 模型，大规模制造机器人，并直接将它们部署到供应链中规模最大的企业。也许可以聊聊这个——你知道，我们看过很多项目推介，有很多公司都说自己在做机器人，但实际上并没有部署机器人，或者还没有开始部署。是吧。So we're about 160 people based primarily in Boston and we're training frontier robot AI models manufacturing robots at scale and directly deploying into the largest businesses in the supply chain. Maybe talk a little bit about like you know we see a lot of pitches there's a lot of companies that talk about doing robotics but don't actually deploy robots or have not yet. Yeah.

而且你们是少数几家真正大规模部署机器人技术的公司之一。所以，也许先聊聊公司的业务现状吧。嗯。And you're one of the few companies has actually deployed robotics at scale. So maybe just talk about like the state of the business. Yeah.

呃，我们在美国各地都部署了机器人，服务对象从小型夫妻店到《财富》50强和《财富》100强企业。Claude 昨晚告诉我，它估计 Tutor Robots 已帮助制造并运送了超过2.5亿件产品。哇。Uh, we have robots deployed across the United States everywhere from small mom and pop businesses to Fortune50, Fortune 100 businesses. Claude told me last night that it estimates Tutor Robots have helped manufacture and ship over a quarter of a billion products. Wow.

面向美国消费者。所以，可能大多数美国消费者在下游几层都有一位导师客户。嗯，我想这源于这家公司的某种起源故事：我们当时在观察机器人学习领域的现状。To American consumers. So like presumably most American consumers have are a tutor customer a couple layers down. Um I think this comes from kind of the origin story of the business which is us looking at the state of robot learning.

我们很清楚，机器人智能和机器人软件才是瓶颈，对吧？过去60年来，机器人一直都是定制工程化的机器，无法规模化。嗯，是的，所以我们创立公司时的核心设想是：只要能开始部署机器人，我们就能构建用于训练这些模型的标注数据集，并为美国消费者创造这样的价值。It was obvious to us that robot intelligence and robot software was this bottleneck right? Robots for 60 years has been this custom engineered machines that can't scale. Um yeah, so we we started the company with this thesis that if we could just start deploying robots, we could build uh the label data sets that help us train these models and deliver this value to the American consumer.

先从任务出发，而不是从模型出发。Start with the job, not the model

我们听过很多机器人项目的路演。99% 的项目一开始谈的都是对模型的一些见解，以及你们是如何构建这个模型的，或者是对数据形态的一些见解。呃，很少有人会去想，归根结底，机器人到底应该做什么。你们是反过来思考的，你们意识到，呃，可以说，你们实际上可以教机器人。没错，这也是你们公司的名字。We see a lot of robot pitches. 99% of them start with some insights about the model and how you got to build the model or some insight about the data uh shape of the data that is and very few people thinking about the what the robot is supposed to do at the end of all of this. You started from the reverse and you had the insight that uh you could actually tutor the robot so to speak. Yeah, that's the name of your company.

这个，呃，洞见是从哪里来的？呃，它来自这样一个思想实验：我们有了这么一个洞见，嘿，地球上有 80 亿人，他们都拥有我们希望机器人具备的基础身体智能。想象一下，如果你有功能无限强大的软件，可以将地球上的任何一个人连接到任何一台机器人上，并让那台机器人拥有智能。Where did that uh insight come from? Uh it came from kind of this thought experiment where yeah, we had this insight that hey, there are 8 billion people on earth who all have this baseline physical intelligence that we want our robots to have. And so imagine you had infinitely powerful software that could connect any human being on earth to any robot and kind of embody that robot with intelligence.

这有点像 ASX 对那种突然拥有无限机器人智能的情况的嘲讽。假设你把这视为理所当然，那接下来呢？That's kind of like this ASX mocking of like suddenly you have unlimited infinite robot intelligence. Let's say you take that for granted. Now what?

对吧？你知道，问题其实还没有真正解决，对吧？你仍然需要真正实现机器人的规模化制造，对吧？如今在美国，机器人还是个小众产业；即使是在制造、物流这类自动化程度最高的行业里，机器人所占的比例也微乎其微。所以这方面还不存在成熟的规模，当然，相关软件也不存在，客户也不存在，因为你知道，机器人还做不了客户想让它们做的任何工作。所以我认为，机器人技术并不只是技术栈中的某一层：它不只是智能，不只是硬件，也不只是客户。关键在于想办法把这三个部分结合起来；依我看，正是这三者的结合，才能让你在其中任何一层打造出成功的平台。Right? you know the problem isn't actually solved right you still need to actually manufacture robots at scale right robots are this cottage industry today in the United States robots are rounding error even in the most automated industries like manufacturing logistics so that doesn't exist certainly none of the software exists and none of the customers exist because you know again the robots can't do any of the jobs that customers want robots to do so robotics is not I think any one layer of the stack it's not just about intelligence it's not just about hardware it's not just about the customer. It's about kind of figuring out how to put all these three pieces together and it's the combination of all three that actually lets you build in my view a successful platform at any one of those layers.

机器人技术：终极系统问题Robotics as the ultimate systems problem

是啊。我们内部常说，机器人公司是终极的系统公司。是啊。Yeah. Internally, we talk about robotics being the ultimate systems company. Yeah.

而不是非得是其中一个或另一个。不过，你知道这件事有意思的地方是什么吗？嗯，这也是我一直想问你的问题：你的技术背景相当深厚，对吧？你知道，就像……我是说，MIT机器人学，对吧？嗯，但每次跟你聊，我都觉得自己是在跟一个运营人士交谈，聊的都是……比如单位经济效益、运营和供应链。As opposed to being one one or the other. Well, you know what's funny about this though, which is um and it was a question I wanted to ask you is uh you've got a pretty deep technical background, right? you know, and like uh I mean MIT Robotics, is that fair? Um but whenever I talk to you, I feel like I'm talking to an operations guy that talks about like that talk about unit economics and operations and supply chain.

所以我是说，甚至那个负责市场落地的人，负责市场落地的人。所以这有点像是你已经完成了转型。因此，也许这是个比较开放的问题，但我一直想知道，这里更难的问题其实是那些东西吗？不是。So I mean and even the go to market guy go to market guy. So it's kind of you've done this like the transformation is complete and so like may maybe this is kind of an open-ended question but like I've always wondered like is the harder problem here actually that stuff? No.

但、但我觉得，关键就在于你得把这一切都做起来。就像我当时还只是个机器人学家时，对吧，只专注于这个问题的技术方面。我觉得我作为机器人学家的优势在于，我的工作涵盖面非常广，是全栈式的。我愿意去考虑制造和硬件这些方面。But but I think you have to do all of it is kind of the thing. And like I when I was just a roboticist, right, and only working on the technology side of this problem, I think my strength as a roboticist was that it was very full stack. And I was willing to think about the manufacturing, the hardware.

我愿意考虑 FPGAAS 和 AS6。比如，如果你像我一样把机器人学看作一个终极的系统问题，那么取得进展的唯一方式，就是尽可能在技术栈的多个层面上找出各种巧妙的权衡。如果你把技术栈中的某一层排除在外，说“嗯，我们不会碰这一层”，那你为了取得进展，就已经排除了 90% 的权衡空间。I was willing to think about FPGAAS and AS6. Like if you view robotics in the same way that I do that it's this like ultimate systems problem, the only way you can make forward progress is by figuring out all of the clever trade-offs across as many layers of the stack as you can. And if you throw out one of the layers of the stack and you're like, well, we're not going to touch that, you've eliminated 90% of your trade-off space to make progress.

有意思。然后开始创业之后，我才真正意识到，哦，好吧，机器人领域所谓的全栈，不只是模型、硬件、软件，还包括供应链、客户和产品，对吧？Interesting. And then starting a business, it became really clear to me that like, oh, okay, robotics is not actually full stack like model, hardware, software. It's full stack like model, hardware, software, supply chain, customers, product, right?

这就完全类似于你会问的那个问题：为什么机器人领域一直没能成功，对吧？机器人初创公司已经有很多了。我认为，没有哪家机器人初创公司真正取得过大规模的经济成功。It's like exactly and similarly you ask the question like why hasn't robotics worked, right? There's been a lot of robot startups. I would argue no robot startup has been like really massively economically successful.

嗯，为什么会这样？我想，第一，是因为你没有一个核心基础要素，也就是智能，而它能让你把整个系统的单位经济效益提升到一定水平。嗯，第二，是因为这是一个极其分散的市场，参与者多到数不胜数，而且他们都在试图彼此协作。而现实中，我们在机器人领域需要的是能够在整个技术栈范围内权衡取舍。Um why is that? And I I think it's because one, you don't have this core primive which is intelligence and that lets you kind of bring the the um unit economics of the system to a certain level. Um but two, it's that it's the super sort of disagregated market and you have infinite players and they're all trying to work with each other and the reality of what we need in robots is to be able to make trade-offs across the full stack.

你知道，这很有意思。这是我们一直以来的一个观察，用这种方式来表述也挺有意思：大多数机器人公司一开始都是横向发展的，后来都会转向垂直领域。是的。You know it's interesting. So this is an observation that we have had and it's it's interesting to articulate it this way which is most robot companies start horizontal and they all go vertical. Yeah.

他们每一家基本上都是这样起步的：我们有一个模型，或者什么的，然后你知道，他们就开始买3D打印机。你觉得这是不是因为市场还很不成熟，就像汽车发展的早期那样？还是说这其实是这个领域固有的特点——也就是说，即使再过20年，你还是得垂直整合，只是因为运动经济学之类的因素要求如此？不，我觉得这是市场成熟度的问题，在早期的计算机市场也能看到类似的情况。所以对我们来说，问题就变成了：好吧，你该如何构建通用平台，让人们能够打造并非机器人领域的企业，对吧？Every one of them like they literally start like we have a model or whatever and then you know they start like buying 3D printers and do you think that that is kind of because the market is so immature like you had to do in the early days of cars or like you think it's actually inherent in this domain like like even in 20 years you will have to verticalize just because the economy of movement or something requires that no I think it's a maturity in the market and you can see similar things in like the early computing market. And so to us, the question then becomes, okay, how do you build kind of the general platforms that let people build businesses that aren't robotics businesses, right?

如今我们有这么多了不起的软件企业，而经营这些企业的人不必去考虑芯片架构，对吧？嗯，如何才能在物理世界中借助机器人实现这一点？又该如何构建这样的平台？我同意你的看法，人们一直试图通过只关注技术栈中非常狭窄的层面来解决这个问题。We have all these incredible software businesses today and the people who run them don't have to think about chip architectures, right? Um, how do we make that possible in the physical world with robotics and how you go about building that platform? And I agree with you, people have tried to solve that problem by thinking about very narrow layers of the stack.

在我们看来，现实情况是，你需要构建一个全栈平台。而要知道这个全栈平台是否真的有效，唯一的办法就是让成功的业务部门在它之上运营。我的意思是，我是说，你是否愿意接受这样一种可能：现实世界和数字世界其实并不一样？我们之所以把计算做成横向通用的，是因为有清晰、通用适用的抽象，而在导航物理空间这类事情上，情况恐怕并非如此。不过，我觉得人形机器人的妙处就在于，它也是一种清晰的抽象，对吧？想想 CPU 是什么：它是一台通用计算机器，当然并没有针对每一种用例都做到极致优化，但它足够通用，能够运行大多数计算任务。And in our view, the reality is you need to build a full stack platform. And the only way to know that that full stack platform really works is to actually have successful business units operating on top of it. I mean I mean are you open to like it being the case that actually the physical world is different than the digital world and the reason we horizontalized compute is because you have clean abstractions that are generally applied and that's just not the case for like navigating you know physical space or I think the beauty of something like a humanoid is it's the same it's that clean abstraction right you think about like what is a CPU and it's this general computing machine and it's not perfectly optimized for every use cases right but It's general enough that you can run most computing worlds.

哦，不过那就是人类内部的横向组件。但比如说，你会用同样的——你知道，大脑，或者说子组件，来造一个人，就像造一架无人机或其他形态的东西一样吗？不会，但你可以把同样的类比用在计算领域，对吧？我们有 CPU、GPU、AS6，对吧？Oh, but that's so that's your horizontal component within the human. But but for example, would you use the same you know brain or you know like sub components to make a human as you would to like make a drone or to make some other form factor? No, but you can make that same analogy in compute right we have CPUs, we have GPUs, we have AS6, right?

所以我认为，你会追踪市场的成熟度，并从专用定制机器这个领域起步，因为市场目前只能支撑这一类产品。之后你会转向通用型产品；在机器人领域，这会表现为人形机器人以及类似的形态。最终，你会在某种程度上突破到，呃，你知道，那个 resp.So I think you track the maturity of the market and you start in this world of specialization custom machines because that's the only thing the market can support. you move towards general purpose and in robotics this is going to look like humanoids and similar form factors and then eventually you kind of break past to uh you know that resp.

为什么叫 Tutor，以及导师是否会消失Why it’s called Tutor, and whether tutors go away

为什么这个东西叫 Tutor Intelligence？Tutor 创立时的理念是：整个互联网中蕴藏着人类智能，人们在社交媒体上发帖、写邮件，而这个人类智能网络自然产生的内容，为 LLM 和其他数字世界模型创造了一个很棒的数据集，我们可以用它来训练通用模型。问题在于，我们在物理世界中的工作并没有以类似的方式产生这些内容，对吧？Why is this thing called tutor intelligence? So, Tutor was founded uh kind of with this thesis that there is a whole internet of human intelligence that isworked right that is posting on social media that's writing emails and that network of human intelligence for LLMs and other kind of digital world models by natural emission has created this beautiful data set that we can use to train general purpose models. And the issue is our work in the physical world is not similarly, right?

比如我拿起咖啡杯，没人知道，对吧？也没人记录我所有指尖的受力情况，以及它们在不同姿势下的位置。所以我们没有这项资产。Like I pick up my coffee cup and nobody knows, right? Like nobody recorded all of the pressures of of my fingertips and where they were in the poses. And so we don't have this asset.

嗯，Tutor 背后的核心想法是：如果我们不再试图寻找某种算法，让机器人在没有任何数据的情况下也能获得智能呢？而我们当时就觉得，没错，这看起来像是一个对我们不利的问题，对吧？计算机视觉领域不是这么发展的。Um and the core idea behind tutor is what if instead of trying to fill find kind of this algorithm where from no data we could build robot intelligence. And for us we were like yeah that that seems like a problem that's rigged against us, right? That's not how this worked in computer vision.

这并不是 NLP 的运作方式。如果我们能在现实世界中构建这样一个机器人网络呢？我们可以让它们与人类远程操作员连接起来，我们称这些人为导师，嗯，他们教机器人如何执行新任务。It's not how this worked in NLP. What if we could build this network of robots out in the real world? We could connect them with human teley operators, which we call tutors uh that teach the robots how to do new tasks.

同样，互联网在某种程度上也是出于使命感，才创建了这个数据集来训练这些前沿模型。我们也可以用同样的方式对待机器人，打造一个机器人训练数据互联网。这样一来，就会涌现出各种美妙的特性：比如，突然间，这就不再是一个需要巨额资本投入的项目了。我们要做的是搭建这个网络，而且在此过程中，你也创造了巨大的经济价值，对吧？And in the same way, the internet kind of by a mission created this data set to train these frontier models. We can do the same with robots and we can build this internet of robot training data. And there all these beautiful kind of emerging properties where like okay, suddenly it's not this massive capital project. it is to go build this network, but also you're creating incredible economic value as you're doing it, right?

随着你部署这些机器人并收集这些数据，每个人的商品都会变得更便宜。所以我认为，这以一种非常独特的方式创造了正和效应。嗯，所以我们当时就在想，好吧，我们要给这家公司取个什么名字？You're making everybody's goods cheaper as you deploy these robots and collect this data. And so it's very positive sum, I think, in this very unique way. Um, and so when we thought about, okay, what are we going to name this business?

嗯，你知道，这严格来说并不完全是人工智能。里面确实有一些人工智能，但归根结底，我们构建的是一种类似导师智能的东西，它负责连接并控制这些机器人。是啊，这是个惊人的洞见。Um, you know, it's not strictly artificial intelligence. There's some artificial intelligence, but it was ultimately like this tutor intelligence that we're building that was connecting and controlling these robots. Yeah, that's an amazing insight.

有意思的是，在我们投资之前和客户交谈时，他们大多似乎不知道，甚至不在乎有没有导师、导师什么时候在工作，以及你们的学习模型什么时候在运行。也要归功于你们，把这一切做得非常、非常无缝。是啊，对我来说，这是个信号，说明你们的机器人做得不错，对吧？你希望得到的反馈应该像是：我批量生产的洗发水就是这样的。And the funny thing is when we talked to our customers before we invested, most of them didn't seem to know or didn't even seem to care that there was a tutor or when the tutor was operating and when the your learning model was operating and that credit to you guys that you made this very very seamless too. Yeah, this is to me a signal that you know the robots are good, right? You want the feedback to be like my shampoo is that I'm manufacturing at volume.

现在比之前便宜了5美分。如果反馈是说机器人很漂亮，那它可能是个非常糟糕的机器人。目标是未来不再需要导师吗，还是这无关紧要？It's now 5 cents cheaper than it was. If the feedback is like the robot is pretty, it's like a probably really bad robot. Is the goal to not have tutors in the future or does it matter?

我们会从非常务实的角度来思考，比如辅导老师的运营复杂度有多高？他们会带来怎样的经济影响？对吧？We think of it like at a very pragmatic level of like what is the operational complexity of tutors? What is the economic impact of tutors? Right?

比如说，想想用一台 H200 来提供模型服务的成本是多少，对吧？对吧？然后你可以算出这些成本，以及这台 H200 的占空比和利用率。Like think about what is the cost to serve models with an H200, right? Right? And you can math that out and what's the duty cycle and utilization of that H200.

你也可以用非常类似的方式来思考人类智能，对吧？你真正希望人类具备怎样的多元智能特征，而这些是你目前还无法从模型中获得的？嗯，然后这就成了一个单位经济效益问题，对吧？And you can think very similarly about human intelligence, right? What is sort of the heterogeneous profile of intelligence that you really want from humans that you can't get yet from your models? Um, and then it becomes a unit economics question, right?

比如说，和使用辅导老师相比，你得投入多少工程工作，才能让模型再多达到一个“9”？说到底，你知道，回到我们刚才聊的话题，你肯定希望尽可能多地在这里做出各种取舍，让这样的业务能够运转起来。我的意思是，你知道，嗯，人们很容易会说，生成式 AI 创造的那种经济奇迹也可能在这里重演。Like how much engineering do you have to do to get that last nine uh out of your models versus using tutors? And really, you know, going back to what we were talking about, you want to be able to make as many trade-offs here as you possibly can to make a business like this work. I mean, the, you know, um, it's tempting to say that like the economic marvel that happened with generative AI could also happen here.

生成式 AI 令人惊叹的一点是，你训练这些模型时用的是某种数据分布，结果发现，基本上同一种分布在经济上是可行的，对吧？比如，你用图像训练它，人们就想要图像；你用文本训练它，人们就喜欢文本。这有点像是输入什么分布，就输出什么分布。但对这些机器人来说，情况是否如此还不明确，对吧？你可以用某种数据分布来训练它，但实际上你需要的东西可能只是稍微超出这个分布的范围。所以，我不知道，你大概最清楚这如何扩展、收敛情况如何，以及你们是否真的能取消导师。你对此有什么直觉？And the kind of the the marvel that happened with generative AI is like you you train these, you know, these models with, you know, some distribution of data and it turns out basically the same distribution is economically viable, right? It's like you train it on images and people want images. you trade it on text and people like you know it's like kind of distribution in distribution out. It's not clear that this is actually the case with with these robots right you could train it on like you know a distribution of data but then like actually what you need is just slightly out of that distribution so like how I don't know you probably have the best visibility into how this scales and what the convergence is and whether you can actually remove tutors like what is your intuition on on that?

我认为这是个重大问题之一，而且归根结底，我觉得关键在于：数据是什么？你又是从哪里获得这些数据的？是的。互联网的神奇之处就在于，如果你是一家公司，在应用层为软件工程师提供工具，帮助他们加快软件开发，那么大量极具经济价值的工作自然就会流向你。I think this is one of the big questions and I think really it ultimately comes down to like what is the data and where are you getting it from? Yeah. And the magic of the internet right is like if you are um a company and you provide at the application layer tools for software engineers to accelerate their software development you are naturally inflow of all of this really really really economically valuable work.

对吧？如果你能靠做生意赚钱，那你很可能也在靠做生意生产数据。对吧？这些经济激励是一致的。Right? If you can make money as a business um then you're probably also making data as a business. Right? those like economic incentives are aligned.

机器人也是一样，如果你想：“好吧，我们就把摄像头绑在每个人头上，然后把他们派到印度尼西亚各地，让他们做 X、Y、Z 来收集数据。”你没有任何保证，对吧？你可以猜，但市场那只看不见的手并不会指引你，确保这些数据确实具有经济价值。And similarly for robots, if you're like, "Okay, we're just going to strap cameras on everybody's heads and send them throughout Indonesia to collect data doing X Y and Z." You you have no guarantee, right? You you can guess. Um but there's no, you know, invisible hand of the market that's guiding you to ensure that that data is actually economically valuable.

但如果它实际上是某种排放副产物，而你在真实客户现场部署了机器人，客户付费让这些机器人执行任务，那你就能确定这些数据具有经济价值。不过，这不也在某种程度上取决于解空间的维度吗？比如说，经济上有价值的东西可能维度非常高，以至于你也许能解决其中的 90%，但剩下的 10% 还得暂时由人来完成，因为要获得那部分数据集，需要投入太多导师、花费太长时间。这样也没关系，对吧？你可能愿意接受这种经济上的权衡。我觉得这也可能是其他形式的数据发挥作用的地方。我想我们不是纯粹主义者——再说一次，我们愿意接受任何权衡；我们很喜欢利用仿真来拓宽这些数据分布。But if it's actually this like kind of emission byproduct, if you have robots in real customer sites that customers are paying for, for those robots to do that task, you know for sure that that data is economically valuable. Doesn't it kind of depend on also the dimensionality of the solution set though? Like it could it could be the case that like the economically valuable stuff is so high dimensional that you could like you could solve 90% of it but the last 10% you know you'll you'll need a human being to do for a while just because like getting that data set is just going to require too many tutors over two period of time and that might be fine right you might be willing to accept that on the economics that also might be a place where other forms of data matter I think we're not purists uh again we're willing to take any trade-off right we love using simulation to kind of widen these data distributions.

我们很喜欢利用比如互联网规模的数据来拓宽这些分布。除了真实世界的机器人数据，我们还有很多其他工具可用。We love using like internet scale data to widen these distributions. So we have a lot of tools in our toolbox other than that real world robot data.

前沿模型如何加速机器人技术发展How frontier models are accelerating robotics

再多聊一点模拟，以及这些其他工具，还有像 Astra 这样的影响模型正在产生的作用。这似乎又成了机器人领域一股令人兴奋的新兴热潮，对吧？你们是怎么用它的？这确实是一个巨变。我觉得我们在这样的世界里待了一段时间：大家都知道，关于世界的这些通用知识都编码在这些模型里，但它们却无法真正表达出来，对吧？它们还缺少空间推理这最后一环，无法把一个 GBT 模型所知道的东西，和动作扩散 Transformer 头所能知道的东西联系起来。嗯，不过现在情况变了，对吧？and talk a little bit more about the simulations and these other tools and the impact models like Astra are having on that seems to be another cottage industry of excitement for the robotics world right how are you using that it's it's a sea change right I think we lived in this world for a while where everybody knew that all of this general knowledge about the world was baked into these models but they couldn't really express it, right? They didn't have that last mile of spatial reasoning to connect the dots from the stuff that like uh you know a GBT model knows to something that an action diffusion transformer head can know. Um and that's changed, right?

各家实验室现在正在用 CAD 等 3D 空间数据对模型进行后训练，还有，嗯，像 3D 建模这样的数据。顺便说一下，他们这么做是因为有直接的经济动力，毕竟确实存在这样的市场，所以他们可以马上将其变现。结果发现，这恰恰是我们的模型所需要的空间推理能力。所以我认为，我们仿佛一下子被推进了未来很多年，因为我们开始能够将这些大型前沿模型所具备的通用智能用于机器人技术。The labs are now post-training their models on 3D spatial data like CAD and um like 3D modeling, which by the way they're doing because there's this direct economic incentive because there's a real market for it. um so they can immediately monetize it. And it turns out that's the exact spatial reasoning that our models need. So I think we've been catapulted many years ahead in the future because we're starting to be able to use all of that general intelligence that lives in these big frontier models for robotics.

而且因为这是一个系统性问题，对你的整体影响是加速性的。对。相较于拖慢你的速度，或者说有人会来抢你的饭碗。And because it's a systems problem, the net effect for you is acceleratory. Yeah. As opposed to slowing you down or saying somebody's going to come and eat your lunch.

是啊。不是，我们，呃，也见过几次这种情况发生，对吧？我们在 2021 年创办了这家公司。Yeah. No, we uh and we've seen a couple of these uh happen, right? We started this business in 2021.

我们最初是用卷积神经网络来做这件事的，对吧？所以我们已经经历了每一次 C 变更，而始终有价值的东西就是数据流，对吧？数据流始终是你能够独有地做到的事情。We were doing this initially with convolutional neural networks, right? So like we've we've gone through every C change and and the thing that is always valuable is the flow of data, right? And the flow of data is is always the thing that uniquely you can do.

是啊，那些五花八门的架构技巧，如果能帮你加速飞轮运转，那就去做，对吧？如果能帮你部署更多机器人、收集更多数据、扩大制造规模、执行更多任务，那就值得做。不过，归根结底，我们认为，一家企业真正具有长期、代际价值的，是飞轮和循环本身。And it's like yeah, those like the a million architectural tricks like do them if it helps you accelerate the flywheel, right? if it helps you deploy more robots, collect more data, manufacture at a bigger scale, do more tasks, like they're worth doing. Um, but but ultimately we think the thing that has kind of the long-standing generational value in a business is the flywheel and loops themselves.

内部自主研发机器人：Cassie 和 SonnyBuilding robots in-house: Cassie and Sonny

你能不能稍微讲讲，你说自己造机器人时指的是什么？我觉得这个连续谱的一端，大概就是你买一台宇树机器人。嗯。Can can you talk a little bit about what you mean when you say you build robots? Like I think on one end of the continuum it's like you buy a unit tree. Yeah.

你知道，你会提示它，然后在另一端——你知道，比如你在加工零件之类的，这事儿你们现在进展到哪一步了？对。所以，给大家一点背景：2025 年，美国部署了大约 3.8 万台工业机器人，这个数字很小，尤其是跟大致数一数那些 FANUC 机器人和老款 FANUC 机器人相比，对吧？但情况就是这样，对吧？在美国，没有人部署过任何 Unitree 机器人；如果有的话，它们就会出现在这些数字里，但很可能数量为零。嗯，所以现有的机器人供应链规模太小，而且正如你指出的，它的方向也不对，对吧？比如 FANUC 和 ABB 正在制造的那些机器人本体。You know, and you like prompt it and the other end, you know, like whatever you're like machining parts like where are you in this? Yeah. So um for context for everybody in 2025 the United States deployed uh 38,000 industrial robot workups which is tiny compared to roughly counting the fuks and the old the fukes right but but that's it right nobody nobody deployed any unitries in the United States and to any they would have shown up in those numbers but they were probably zero um and so the supply chain that exists for robots is both too small and as you called out wrong right like the robot spots that Fenuk and AB are building.

他们大多不是在美国制造这些产品，但即使他们确实在美国制造，也是在制造专为系统集成商设计、适合快速重复动作的设备。这些设备的设计并没有考虑到你希望与前沿模型协同设计时所需的那些权衡。因此，在硬件方面，我们基本上得从头开始。They're mostly not building the United States, but but even when they are building them in the United States, they're building these objects that are designed for systems integrators, that are designed for fast repetitive motions. They are not cut from the set of trade-offs that you would want to co-design with frontier models. Um, and so we're kind of starting from scratch on the hardware side.

嗯，所以 Tutor 为了大规模部署机器人，实际上不得不自行设计和制造硬件。就我们而言，现在我们并不制造执行器，对吧？我们也不制造摄像头。有整整一类产品是我们从 OEM 厂商采购的，而他们的制造规模要大得多。Um, so tutor in order to deploy robots at scale has actually been forced to design and manufacture our own hardware. for us today, like we're not building actuators, right? So, we're not building cameras. There's a whole layer of stuff that we're sourcing from OEMs where they're manufacturing at a much larger scale.

不过，真正的机器人本体——就是那个能到处移动、装有机械臂、能完成所有任务的东西——我们实际上正在马萨诸塞州沃特敦大规模制造。你能谈谈机器人的形态吗？那叫什么来着？But the actual robot itself of, you know, the thing that drives around and has the arm on it and can do all the tasks we actually are building uh at scale uh in Watertown, Massachusetts. Can you talk about the morphology of the robot? Is that what is that called?

形态学、解剖学都可以。你可以用任何方法。它们看起来是什么样？Morphology, anatomy. You can use whatever. What do they look like?

它们长什么样？我们做两个。嗯，我们做一台机器人。What do they look like? We make two. Um, so one robot we make.

看看他的网站，伙计。嗯，继续。呃，Cassie，Cassie算是个蛮横的人，对吧？Look at his website mine. Yeah, go ahead. Uh, so Cassie, Cassie is kind of uh a brute, right?

Cassie 是为重型作业而设计的。它有一条机械臂和许多吸盘，由基础模型驱动，模型规模大概在 2 亿到 4 亿参数之间。它也算是诞生于这样一种理念：Tutor 于 2021 年创立时所处的那个领域一直在思考，基础模型能为机器人做些什么？它们可以预测可供性和抓取方式，然后机器人就可以据此执行。Cassie is designed to do heavy duty work. It has a single arm and a lot of suction cups and it's powered by foundation models and like the 200 to 400 million perimeter range and kind of came out of this world that tutor was started out of in 2021 of like what can foundation models do for robots? They can predict kind of affordances grasps that you can go then execute.

嗯，这其实是硬件的协同设计：既要能大规模制造，也要考虑你的模型实际能做到什么；同时还要满足一个巨大的市场需求。在美国，实体经济中的任何东西都必须用托盘或纸箱运输。因此，搬运托盘和箱子这类工作最终就需要大量人工来完成。Cassie 可以说就是这种协同设计的成果。我们大规模制造它，并将其部署到制造业和物流业中种类繁多的企业。Um, so it's this co-design of the hardware that you can manufacture at scale, what your models can actually do, and actually a big market need where in the United States, anything in the physical economy has to be transported on a pallet or in a cardboard box. So there ends up being an enormous amount of physical human handwork of transporting pallets, cases um, by hand. And so Cassie is kind of the result of that code design. and we manufacture it at a very large scale and we deploy it to a big diversity of businesses across manufacturing logistics.

我们的第二台机器人叫 Sunny，Sunny 算是一种工业半人形机器人，对吧？这就好比你在看一台设备时说：“我需要这台机器人在工厂里以人类的工作速度和可靠性运行 3 到 10 年。”这样一来，最终做出的设计决策就会有所不同。Our second robot is called Sunny and Sunny is kind of an industrial semi-humanoid, right? It's as if you said you were looking at a unit and you were like, I need this robot to run for 3 to 10 years in a factory at human rates and reliability. And so you end up with a slightly different set of design decisions.

Sunny采用轮式而非足式设计，因为大多数室内工业环境的地面都经过抛光。它的载荷能力高得多，移动速度也快得多，并且符合工业协作安全标准，因此能够在人类周围作业。目前，我们有100台Sunny在波士顿的数据工厂运行，收集供内部使用的数据，并推进我们的研究计划。如今，我们还在真实世界中部署了一支不断壮大的Sunny机队，执行更加灵巧的双手操作任务。Sunny is wheeled, not legged, because most indoor industrial settings have polished ground floors. um it has a much higher payload capacity and can move much faster and has uh industrial collaborative safety standards that enable it to operate around humans. And so we have 100 sunnies operating in Boston in our data factory collecting data for internal use and advancing our research agenda. And then we also now have a growing fleet of sunnies out in the real world uh doing more dextrous by manual manipulation.

那么，你觉得这条涵盖不同机器人形态的产品线需要有多广？其中三种能覆盖很大一部分有价值的市场吗，还是需要30种？这取决于你如何定义有价值的市场。And do you see how broad do you see this product line of different robot form factors? Is it can you capture a big chunk of the useful market with three of them or do you need 30 of them? Depends how you define the useful market.

比如，你想往哪个方向发展？如果你只想深耕制造业物流领域，Sunny 和 Cassie 现在大概就能完成 80% 到 90% 的工作，对吧？它们就是为这些细分场景设计的。Like where do you want to go? If you want to just live inside of manufacturing logistics, Sunny and Cassie today can probably do 80% to 90% of the work, right? These are designed for those cuts.

呃，或许你可以简单说说那项工作是什么，因为，是啊，我不确定大家是否普遍了解。对。所以，大多数人对美国的工业基础或制造业物流是什么样子，都有一种印象，而这种印象来自电视节目《How It's Made》，呃。Uh what maybe just describe what that work is because yeah I'm not sure it's broadly understood. Yeah. So most people have this mental picture of what sort of the industrial base or manufacturing logistics look in the United States which comes from um the TV show how it's made.

我不知道你们两位是否见过这个。见过。当然。I don't know if either of you have seen this. Yes. Of course.

当然。是啊。你会看到那种鲁布·戈德堡式机械装置，还有由杠杆组成的机器，就像是……那些图片里看不到任何人，对吧？Of course. Yeah. And you see these like Rube Goldberg and machines of levers and it's like yeah you don't see any humans in those pictures, right?

有人造了一台机器，它本身就是工厂，而且它确实存在。呃，既然它存在，他们大概拍下来放到《造物工艺》节目里了，对吧？因为那可是美国制造业和物流业中顶尖的 0.01%。Somebody built a machine that is the factory and that does exist. Uh, and if it exists, they probably filmed it and put it on how it's made, right? Because that's the top 0.01% of American manufacturing and logistics.

呃，那下面是什么呢？你走进这些占地百万平方英尺的工厂，就会看到一群人站在传送带或折叠桌旁，手工做着各种事情。对于任何不是来自这个行业、不了解这个圈子的人来说，这都相当令人震惊。Um, and so what's below that? And you walk into these million square foot facilities and you see a bunch of people uh standing around on conveyor belts or folding tables doing things by hand. And it's kind of shocking for anybody who doesn't come from this industry and community.

嗯，所以你看看像 Cassie 这样的机器人，就会发现这类工作工作量巨大，而且非常辛苦，很难招到人。员工每三个月就会更替一次，而从事这项工作的员工平均年龄是55岁，眼看就要退休了。所以我们想，好吧，我们可以用 Cassie 来做这些辛苦的工作。Um, and so you look at a robot like Cassie and it's like, you know, a huge amount of work, which is backbreaking work that's really hard to hire for. There's a three-month turnover where your average age of the worker doing that work is 55 years old and about to retire. And it's like, okay, we we could use a Cassie to do that backbreaking work.

同样，Sunny，基本上你买过的任何装在盒子里的东西，都得有人把它装进盒子里——这对大多数人来说简直是个惊人的发现。呃，任何需要一定程度组装的东西，对吧？比如说，连 iPhone，你可能都会以为是机器人把包装组装起来的。不是，是人，哪怕规模已经大到这种程度。And similarly, Sunny is like basically anything you ever bought that came in a box, a human had to put that in a box, which is a crazy revelation for most people. uh anything that requires any degree of assembly, right? Like even, you know, iPhones, you know, it you would assume it's a robot, you know, putting that packaging together. No, it's a person even at the highest level of scale.

然后说到他的问题，就是你有那种带吸盘的单臂机器人，还有双臂人形机器人。对。它们有什么做不到的？And then to his question, which is so like you have like the single arm with the sucker, you've got the two armed humanoid. Yeah. What can't they do?

是啊。他们有什么做不到的？接下来你打算怎么办？Yeah. What can't they do? And where do you go from there?

是啊。所以它们不能到外面去，而外面的世界真的非常有意思。哦，那只是行动能力方面的问题吗？Yeah. So they can't go outside and outside is really really interesting. Oh, is that just like a locomotion issue?

这不是移动方式的问题，是硬件的问题，对吧？这些这些机器人有轮子，而且有一点牵引力，你知道，呃，它们还有这种能活动的后腿，但是呃，它们没法在碎石路上行驶，对吧？It's not locomotion. It's the hardware, right? These these robots have wheels and they have like a little bit of traction, you know, uh they have like these articulating hind legs, but uh they can't drive over gravel, right?

机器人在建筑领域有巨大的发展机会，比如用机器人从垃圾填埋场回收材料。嗯，我们，你知道，这需要有腿的机器人。所以我认为，这最终会成为我们具身形态谱系的一部分。There's huge opportunities of robots in construction, robots reclaiming materials from landfills. Um we, you know, we need robots with legs uh for that. So, I think that'll eventually be part of our pantheon of embodiment.

同样，家用机器人也是如此。你现在大概不太想在家里放一台 Cassie 或 Sunny。它作为一种设备还是有点太工业化了。And then similarly, robots in the home. You probably don't want a Cassie or Sunny in your home just now. It's a little bit too industrial of a device.

是啊，它们看起来挺吓人的。是啊。Yeah, they look scary. Yeah.

向首次购买机器人的客户销售Selling to first-time robot buyers

是啊。我们来聊聊客户采用这方面吧。进展怎么样？Yeah. Let's talk about the customer adoption side. How's how has that been?

显然，不完全是从一个……我没有人手，所以能拿到什么就拿什么。对，等他们把它放进装配线，或者不管那是什么。Obviously, not strictly from a I don't have people so I'll take whatever I can get. Yeah. To once they have it in their assembly line or whatever it is.

他们在心态上是如何接受的？嗯，是的。这是个有意思的挑战，因为我们卖机器人给的客户几乎都从来没有买过机器人。How does adoption go uh in terms of their mindset? Yeah. It's an interesting challenge because almost everybody we're selling a robot to has never purchased a robot before.

嗯，这种情况其实可以从这些统计数据中看出来，对吧？如果美国每年只部署 38,000 台机器人，如果美国十家工厂和仓库里有九家没有任何机器人，那么几乎整个市场都是一片尚未开发的蓝海。而这些人呢，了解传统自动化的那一套是什么样的，所以他们会觉得机器人不适合自己。Um, and that kind of comes out of these stats, right? If the United States is only deploying 38,000 robots a year, if nine out of 10 US factories and warehouses don't have any robots, then almost all of the market is green field. And these people, you know, understand what this old guard of automation looks like and they assume a robot doesn't work for them.

所以，我们真正突破这一点的方式，并不是谈论 AI、模型、辅导老师之类的东西。我们打电话给他们，说：“嘿，你有没有考虑过雇一个时薪 16 美元的机器人？”他们以前没考虑过，对吧？对他们来说，这是个全新的提议，对吧？And so the way we break through that actually isn't by talking about AI or models or tutors or anything like that. We called them up and we're saying, "Hey, have you ever considered a robot for 16 bucks an hour?" And they haven't, right? That that's a new proposition for them, right?

所以，你可以预约一次会议，然后向他们大致介绍我们正在做的事情，让大家感兴趣并参与进来。嗯，通过构建这样一种商业模式，让我们作为机器人供应商与客户之间真正实现激励一致，我们就能以远高于传统工业自动化行业的速度推广机器人，快出好几个数量级。我们从首次通话到签订机器人合同，平均需要40到60天。So, you can book a meeting and then you can kind of explain to them this thing that we're doing and get people interested and involved. Um, and kind of by creating that business model where there is that true incentive alignment between us as the robot vendor and the customer, we're able to run distribution of robots at velocities that, you know, are many orders of magnitude faster than traditional industrial automation industry. We have an average time um from uh that first call to a robot signing contract of 40 to 60 days.

从合同签署到机器人真正进驻客户现场开始工作，平均大约需要40天。呃，我们能在一天内让机器人投入生产。呃，通常机器人到现场后的头几个小时内就能投入使用。We have an average time from that signed contract to robots actually in the customer site of about 40 days doing work. Uh we stand up robots in production within a day. Uh often within the first couple hours that a robot is on site.

所以我们能够非常非常非常迅速地推进，因为机器人不仅是一台具备通用智能和通用能力的机器，我们不会对它进行定制，而是直接从工厂发货给客户。嗯，而且我们不会让客户承担任何经济风险。因此，我们能够争取到客户加入。So we're able to move very very very quickly because not only is the robot this generally intelligent, generally capable machine that we're not customizing, we're just, you know, shipping directly from the factory to the customer. Um, but we're not putting any sort of economic risk on the customer. So, we're able to get them on board.

而且在他们部署之后，他们对人工劳动与机器的看法会发生怎样的变化？运维群体真的非常棒，因为他们看到某样东西能正常运转，就会觉得：它能用，对吧？就是这么回事，对吧？And once they deploy it, how do their attitudes about labor versus machines change? The operations community is really really fantastic because they see a thing work and it's like it works, right? Like that's it, right?

就是，嗯，差不多吧，我我显然看得出来它确实能完成这项工作。我知道所有需要完成这项工作的地方。咱们把它放到所有那些地方去吧。It's it's like, yeah, I I can see obviously it does the job. I know all the places where this job needs to be done. Let's go put it in all those places.

所以，我们能够非常非常快速地扩大规模，既服务小企业，也服务供应链中一些最大的公司——从一个地点部署一台机器人，到在多个地点部署十几台甚至更多机器人。不过这确实挺有意思，因为就像很多生成式 AI 一样，其中有一种社交层面的因素：人们会对自己的模型产生很强的依恋，所以很难换掉模型。我是说，你听着，你现在部署机器人方面的经验恐怕比在世的大多数人都丰富——你是不是也有同样的感受？人们把它们看作设备，还是已经开始把它们看作有生命的存在和同事了？是吧。So we've been able to scale up very very quickly both with small businesses um and to some of the largest companies in the supply chain from you know one robot in one site to you know a dozen plus robots across many sites. It it is interesting though because like like a lot of geni was like there's a social element where like people get very attached to their models and like it's hard to swap models and so I mean listen you probably have more experience deploying robots than most people alive at this point like are you saying the same thing like is it are they viewed like equipment or like they starting to be viewed like beings and co-workers? Yeah.

所以我看到一条特别好笑的推文，大概是这么说的：机器人就是机器人，除非它能干活，那它就是台机器。我得查查这话是谁说的。不过这是真的吗？So I saw a very funny tweet which was um a robot is a robot unless it works in which case it's a machine. I got to figure out the attribution. But is it real?

就是，它……它挺真实的。是这么回事吗？就像它一旦能正常工作，他们就把它当成一件设备来看。Like is it it's it's pretty real. Is that what it is? Like they just view it as like a piece of equipment once it works.

当然是在工厂经理这个层级。对。我觉得还有一个很有意思的层面：在机器人领域，买家不是使用者，对吧？Certainly at like the factory manager level. Yes. I think there's still the layer of like interestingly in robotics your buyer is not your user, right?

你的采购方就像首席运营官或总经理，而用户就像工厂工人，对吧？他们整天都在和机器人一起工作。实际上，你确实需要培养工人与机器人之间的关系，因为你知道，我们不会把工厂百分之百自动化。Your buyer is like the COO or the general manager and the user is like a factory worker, right? Who's working with the robot all day long. And there you actually do need to cultivate the relationship between those workers and the robots because you know we don't fully automate 100% a factory.

当我们在工厂或仓库里自动化一些工作时，你对此有什么理念吗？比如，你会尝试让它们看起来更友好吗？We we automate some jobs in a factory or warehouse. Do you have a philosophy on like how you even approach this? Like do you like try to make them look friend?

你喜欢让他们看起来更像同事，还是不那么像同事？你让他们说话……你在哲学层面上是怎么看待这件事的？嗯。Do you like to make them look more like co-workers or less like co-orkers? You make them talk like like how do you kind of view this philosophically? Yeah.

呃，所以特别特别幸运的是，有一个完整的学术研究领域叫做人机交互（HRI）。我也不知道。就这样。Uh so very very luckily there is a whole school of academic research called hri human robot interaction. No idea. There you go.

是的。很多人花了很多时间思考这个问题，嗯，其中包括不少和我有相同学术背景的人。嗯，还有很多技巧。Yeah. A lot of people have spent a lot of time thinking about this um including a bunch of people from from my academic background. Um and there are a whole bunch of tricks.

所以有个诀窍是，呃，每台机器人都有名字，这样我们就能和机器人建立起关系。它也会给机器人命名。呃，主要是我们的制造技术人员。So one trick is uh every robot has a name and so that lets us actually build relationship with the robots. It also names it. Uh mostly our manufacturing technicians.

所以，好吧，你给它起名字，不是……这不像狗，你养了只新狗就可以给它改名。所以，当客户和 Tutor 扩大合作、再次下单时，他们就可以给机器人起名字。哦，就像你不应该给船改名一样，因为那会带来厄运。So okay so so you name it not the it's not like a dogs when you get a new dog you can rename it. So when a customer expands with tutor when they when they do a repeat order then they're allowed to name the robots. Oh, like you're not supposed to like rename a boat because bad luck.

船名得保留，但狗可以改名。所以，就……对。所以，新机器人可以被……可以被命名。You got to keep the boat name, but you can rename a dog. So, like Yeah. So, the new robots get to be get to be named.

这样就解决了问题，我们也能获得客户。所以，他们给它起了名字后，归属感更强了。That solves it. And we get customers. So, they get a higher sense of ownership now they've named it.

没错。对。嗯，我们收到了非常强烈的信号，比如有人用自己孩子的名字给机器人命名，对吧？Exactly. Right. Um and we get like really strong signals like we had um people name the robots after their kids, right?

呃，人们会用自己最喜欢的足球运动员或流行歌星的名字给机器人命名。嗯，人们会对机器人产生很深的感情，因为机器人最终确实帮得上忙，对吧？它让他们的工作轻松多了。Uh people named the robots after their favorite football player, their favorite pop star. Um, people get very attached because the robot ultimately helps, right? It makes their jobs easier.

如果你是厂里的流水线工人，就不用再干那么多累死累活的活儿了。你还有机会升到更高的职位，而这些地方都招不到人。再说，如果你是工厂经理，工作就会轻松一千倍。If you're a line worker in the facility, you're not doing backbreaking work as much. You're able to be elevated into a higher position and none of these places can hire. And then if you're a factory manager, your job is just a thousand times easier.

你们用的是什么命名规则？对。从你们……我们一开始是按飓风的方式命名，后来我们就有点……所以那不是……对。What naming scheme do you use? Yeah. From when you We started off doing hurricane naming and then we kind of So that's not Yeah.

你没把《HR Eye Book》读明白。嗯，我也不知道。你是个学者。You didn't read the HR eye book clearly. Well, it's I don't know. You're an academic.

客户不知道布兰妮，你知道，是模 26，或者不管怎么说等于 2。不过，呃，我们其实停下来了，因为我们算是经历了一场飓风。我们把字母表用过太多遍，已经没法继续跟踪了。The customer doesn't know that Britney is, you know, nod 26 or whatever equals 2. It's um it's uh but we actually stopped because we kind of went through the hurricane. We went through the alphabet too many times to be able to keep track of it.

好吧。那么你们现在用的是什么？呃，我觉得技术人员就是挑自己喜欢的用。Okay. So, what are you using now? Uh I think the technicians just just pick whatever brings them joy.

我们非常荣幸能够在美国雇用一大批员工，他们正在帮助建造这些机器人，也在给它们起名字。太棒了。We we have a really wonderful and privilege to employ a big staff of people in the United States who are helping to build these robots and they are also naming them. Awesome.

为什么是中小型企业Why small and mid-sized businesses

在我们投资之前第一次做这项工作时，还有一件事让我很惊讶：我们大多数时候跟机器人公司交流时，他们瞄准的都是那些巨型企业，对吧？是啊，就是那些《财富》美国50强级别的大公司，看起来有预算做各种实验之类的。The other thing that surprised me when we first did the work before investing, most of the time when we talked to robotics companies, they're going after these humongous companies, right? Yeah. The big Fortune50 type companies that seem to have the budget for lots of experiments, etc.

是啊。而我觉得你们相当大一部分客户几乎都是这些本地企业。是啊。Yeah. Whereas I think a significant majority of your customers are all these local businesses almost. Yeah.

那这意味着什么？我的意思是，这里面有什么信息吗？还是说这是有意识的策略？是的，原因很多。首先，这就是市场的大部分，对吧？美国企业以及制造业和物流业中，绝大多数都是小企业或中型市场企业。And what does that I mean is there a message in there or or was it a conscious strategy or Yeah, it's a lot of things. One is that's most of the market, right? The vast majority of US businesses and manufacturing logistics are small businesses or mid-market businesses.

嗯，所以如果你想服务这个市场，这就是服务他们所需要付出的代价。我觉得我们创办这家公司时，正好看到很多非常学术化、以供应链为导向的机器人初创公司纷纷成立，但最终并没有真正成功。嗯，而我们有机会去思考：好吧，到底是哪里出了问题？Um so if you want to serve the market, that's what it takes to serve them. I think we also started this company um at a time we're seeing kind of all of these very academic supply chainoriented robotics startups kind of get started and then not really succeed. Um and we we had the opportunity of being like okay what went wrong here?

我觉得机器人公司犯的一个大错是，它们其实并不是真正的产品公司。它们会想，好吧，有家特别大的公司遇到了一个非常具体的问题。他们愿意给我们一纸漂亮的承诺，说会扩大使用规模。于是我们就要花两年时间，打造一套高度定制的系统来解决他们的问题。And one of the big things I think that robotics companies get wrong is they're not actually really product companies. They're like okay some really big company has a very specific problem. They're willing to write us a shiny piece of paper that says they're going to expand with it. we're going to spend two years building a very custom system to solve their problem.

我们会把它部署上线。它很可能不会奏效，因为任何做过产品的人都知道，产品不可能一次就做成、然后万事大吉，对吧？做产品需要不断迭代。We'll deploy it. Chances are it won't work because like anybody who's built products knows that you can't just one and done a product, right? Doing product requires iteration.

然后，即便它确实奏效了，谁又能保证那个客户现在还有这个问题呢？所以，砰，公司就完了。我觉得我见过这种情况接连发生了五到十次，以至于我想，天哪，要是这种事发生在我们身上，那可真是太丢人了。嗯，所以我认为，与各种各样的客户合作，是一种很棒的推动力，能促使我们打造出形态恰当的产品——对我们来说，就是一款通用机器人，对吧？And then even if it does work, who's to say that customer still has that problem and so boom, the company's done. And I feel like I saw this happen like five to 10 times in a row to the point of like, oh my, if this happened to us, like that would just be embarrassing. Um, and so I think working with a diverse range of customers is this beautiful forcing function for building the right shape of product which to us is a general robot, right?

我们不想打造一台鲁布·戈德堡式机器，只为了让它特别适用于大型工业客户 A 或 B。我们想打造一个平台，让通用能力强的机器人机体运行通用能力强的机器人模型，并能立刻投入工作。嗯，如果你能在两周内和一家中端市场客户签下合同，然后让机器人马上投入运行，而且这类客户和产品与其他客户完全不同，那你就算成功了。We don't want to build a Rube Goldberg machine that works really well for big industrial customer A or B. We want to build a platform that is generally capable robot bodies running generally capable robot models that can immediately get to work. Um, and if you can, you know, sign a contract in two weeks with a mid-market customer and have a robot running immediately after that, and that's a totally different type of product and business than your other customers, you know, you're being successful.

你觉得从长远来看，假设你们能持续取得成功，这个行业会是什么样子？是不是只是说这类工作会变得更便宜、更快，还是质量也会以某种方式提高，或者他们能做更多事情？大概会出现怎样的经济趋同或生产力趋同？嗯，我觉得一切都会变得更好。How do you think in the limit um what the industry looks like assuming you continue to be successful? Is it just that um you know this work gets cheaper faster or does like somehow quality go up or can they do more things or like kind of like what is like the economic convergence or the productive convergence? Yeah, I think everything gets better.

所以，有一整类商品，如果价格更便宜，我们就会买得更多；而如果你认为这些商品有价值，那对所有人都有好处。世界上也存在许多不连续之处。比如，前几天我和一个人聊过，他在威斯康星州一家住宅工厂的生产线上工作。我想，他当时是在描述他们建造的房子：在工厂里建造，成本比传统的现场施工高出大约5%。So there's a whole set of goods that um if they are cheaper we will buy more of them and if you believe those goods are valuable that's good for everybody. There are also a bunch of discontinuities in the world. Like I was talking with somebody um the other day who worked on the line in a housing factory in I think Wisconsin and they were describing how the houses that they're building are about 5% more expensive uh to build them in a factory than to do traditional construction on site.

但如果这些房子的价格哪怕只比传统方式在现场制造或建造的同等品质房屋便宜 1%，开发商的所有需求就会立刻转向这种在工厂里建造房屋的新模式。材料回收、垃圾填埋场以及其他许多市场也是一样。所以我想，我们的希望是，如果 Tutor 真正取得成功，我们就能提供这种机器人基础设施、这支劳动力队伍，让其他企业也能借此改造实体经济，最终让世界成为一个生活成本更低、生活质量更好的地方。But if these houses were even just 1% cheaper than kind of the equivalent quality of house manufactured or constructed on site in the traditional way, all of the demand from developers would immediately go to that other shape of building these homes in a factory. And the same thing could be said about like reclaiming materials and landfills and a whole bunch of other markets. So I think our hope is like if Tutor is really successful, we can provide this robot substrate, this this labor force that other businesses can use to transform the physical economy and make the world ultimately uh a cheaper and better place to live in.

你们会进屋里吗？嗯，会，我们没有什么特别的偏好，哪里都可以去。Will you ever go in the home? Um yeah, we we're not opinionated. We'll go anywhere.

但我想，这在技术上可能会难得多，单从任务的复杂性、安全性以及经济效益来看就是如此。比如说，一台辅导机器人在工厂里能创造多少经济价值？又有多少消费者愿意为它在家里的服务付费？这些账似乎算不过来。不过话说回来，这又回到了我们的整体理念：尽可能在技术栈的多个层面做出取舍，以此站稳脚跟。开始构建平台；随着平台逐步成形，随着时间推移，你需要做出的取舍就会越来越少。机器人领域的产品创新是如何运作的？But I would imagine that that would be kind of a much tougher technical problem just from like I don't know complexity of task and safety and and the economics of it right like how many what is the economic value a tutor robot delivers uh in a factory and how many consumers are willing to pay for that in their home and it doesn't quite work out. Um but again that that comes back to our our overall philosophy is take trade-offs across as many layers of the stack as possible to get a foothold. start building the platform and as you build the platform you have to take fewer and fewer trade-offs over your time. How does product innovation work in robotics?

我的意思是，大多数公司还停留在 V1，而你们已经有两个版本的机器人，并且还在快速迭代。这有什么不同？它和数字产品创新或其他类别相比，有什么区别？I mean most companies are just on V1 whereas you're got two versions of the robot and you continue to iterate really fast. What is it different? How is it different than like digital product innovation or other categories?

我们正努力让它尽可能相似。因此，我们正在制造自己的机器人。这样我们就能掌控整个制造流程，并弄清楚如何每两周对制造流程进行一次调整。We're trying to make it as similar as possible. So we're manufacturing our own robots. This gives us the ability to own that manufacturing process and figure out how do we push changes to the manufacturing process every 2 weeks.

我们每天都会向机器人发布代码，或者每两周发布一次重大版本，并且已经搭建了大量基础设施来支持这样的迭代速度。我认为，我们打造了一种非常独特的产品职能组织形态：在我们的组织中，产品团队就紧挨着客户成功和实施团队，实际上三者作为一个大团队协同运作。因此，他们能尽可能贴近客户。我认为这对我们来说非常成功。We are shipping code to robots either every day or major releases every two weeks and have built a huge amount of infrastructure to support that level of iteration. We've built a think a really unique shape of product function where our product function in our organization actually sits right next to our customer success and implementation functions and operate actually as one big team. So they're as close to the customer as humanly possible, which I think has been really successful for us.

然后我们还有一个研究团队，一方面能够深入融入客户的问题领域，也能调动各种手段，从客户那里获取特定类型的数据；另一方面，也能够去做一些大胆的探索。你知道，如果我们的建模方面取得突破，让机器人能够完成一种全新的任务，我们就可以围绕这一点共同设计产品，我觉得大多数公司都做不到这一点，而且我们能更快地把产品部署到实际场景中。如果我在听这段对话，而且我有一座仓库——是的——想要一台机器人，我只要去网站上填个邮箱就行了吗？And then we also have a research team which is able to be both on the one hand deeply embedded in kind of the customer problem space and we're able to pull all sorts of levers to get specific types of data from customers um but also able to take these moonshots. And you know, if we have a breakthrough on our modeling side that lets robots do a whole new shape of thing, we can kind of co-design a product around that in a way that I think most companies couldn't and get that out into the field much quicker. If I if I'm uh listening to this and I have a warehouse Yes. and I want to get a robot, do I just go to the website and like put in my email?

那么这是怎么运作的呢？你可以访问我们的网站，填写一个邮箱地址。我们还提供很酷的定价计算器，你可以输入一些关于当前仓库或工厂运营情况的数据，就能估算出：嘿，初步部署 TUDA 机器人后，我们每年可能节省 10 万美元、100 万美元，甚至 1 亿美元。That's how does this work? You can go to our website, you can put an email. We actually have cool pricing calculators where you can actually put in some operational data about how your warehouse or factory runs today and you can get an estimate of, hey, with an initial deployment of TUDA robots, we could be saving $100,000 a year, a million dollars a year, $100 million a year.

嗯，而且这不是那种“好吧，这是一个为期五年的定制工程路线图”。而是说，不，你两个月后就能每年省下百万美元，而且不用先投入资本开支。你有没有发现，嗯，这种东西的需求——毕竟它听起来简直像科幻小说——市场上到底有没有需求？Um, and that's not like, okay, this is a road map over 5 years of custom engineering. It's like, no, you could save that a million dollars a year two months from today, uh, without putting capbacks down. And have you found that there is like demand for I mean it's just such a sci-fi thing like is there demand out there?

需求非常旺盛，而且这甚至还不是对机器人的需求，因为再说一遍，人们都不知道这种形态的机器人存在，也不知道它能不能工作，或者能不能胜任这项工作。所以归根结底，我们有一个衡量机器人价值的框架：用机器人能完成的工作量除以它的成本，就这么简单，对吧？其他一切都只是旁枝末节。There is so much demand and it's not even demand for robots because again like people don't know that the shape of robot exists or that it works or that it can do this job. So it's it's ultimately like you know we have this framework of the value of a robot is you take how much work a robot can do and you divide it by its cost and that's it, right? Everything else is is commentary.

所以，如果我们能够在美国实体经济中这些流动性很高的劳动力市场上参与竞争，并且提供能力相当甚至更强、成本更低的方案，那么它的市场就是整个市场，对吧？也就是所有体力劳动。关于这一点，我还有个小小的技术性问题：你说的成本，指的是把前期成本摊销进去之后的成本，还是像设备持续运行的成本？And so if we're able to compete in these highly liquid labor markets that exist across American physical economy, uh, and we can offer something that delivers greater equivalent capability to lower cost, the market for that is the whole market, right? It's all physical work done. And what just one quick wonky question on this, which is how much when you say cost do you mean amortizing the upfront cost versus like the ongoing cost to operate it?

两者都有，这非常有意思，也很不一样，对吧？传统上，我们一直认为机器人技术的投资回报意味着一笔巨额资本支出，然后要花 10 年才能收回成本。但现在其实不是这样了，越来越多的成本都花在持续的推理和远程操作上。不过，这也包括维护、故障维修之类的费用吗？那么，主要的成本是什么？还是说它们大致相当？嗯，随着时间推移，我们会把这些成本都降下来。It's both, which is very interesting and different, right? Traditionally we've thought about robotics as ROI right big capex expense and then 10 years to make it back and now it's actually like no more and more of the cost is going into ongoing uh inference teley operation but does that also include like maintenance and break fix and all of that and then what and like what is the dominant cost or or are they roughly equal? Uh we drive down all of them over time.

不是，不是。我只是想问，从你的角度把销货成本细算一下，前期成本和持续性成本分别占多少？是的，目前持续性成本占得更多，这让人挺意外的。No, no. I'm just saying like from from your standpoint when you like pencil out your cogs, how much of is it the upfront cost versus the ongoing? Yeah, it is more ongoing at this point and that's surprising to people.

有意思。哇哦。是啊。Interesting. Oh wow. Yeah.

我并不觉得意外。我的意思是，你实际上是在和某种你喂它吃米饭、而且效率极高的东西竞争，对吧？比如说，人类的身体。Not surprising to me. I mean, you're really competing with like something that you feed rice to and it's very very efficient, right? Like the the human body.

是啊。那么，这些客户叫你们过去，是因为黑色星期五之类的促销活动快到了，他们需要在很短时间内完成一大批产品，还是说这实际上已经成为他们正式劳动力规划周期的一部分了？不，这是战略性的，对吧？Yeah. So the when you get called into these customers, is it because Black Friday or something is coming up and they need a bunch of units done in a really short time or is this actually now part of their actual labor planning cycle? No, it's strategic, right?

嗯，我觉得这些企业面临一个非常艰难的选择：它们知道员工队伍的年龄结构，也知道由于这些长期趋势，需求正在不断增长，而完成这些工作的成本基础也在上升。于是它们就会想：我们的业务到底算不算得过来？因为你知道，这些不是软件企业，没有那种很高的毛利率可以灵活调整，稍微压缩一点也没关系。情况不是这样——业务要么行得通，要么行不通。It's um these businesses, I think, are faced with a really hard choice, which is they know the age of their workforce. They know that demand is ramping up due to all these secular trends and the cost basis of fulfilling that work is going up and they're like how does our business pencil out because you know these are not software businesses that have these you know huge gross margins that you can you can play with and can compress a little bit and it's like it's fine. It's like no the business either works or it doesn't.

嗯，所以我认为，我们的客户最终想找的是一个能提供机器人解决方案的长期合作伙伴，对吧？如果我们的某个客户生产除臭剂，他们就专注于生产除臭剂，对吧？他们会是除臭剂生产或蜡笔生产方面的运营专家，或者擅长运营他们所生产的其他产品；而他们需要一位机器人领域的专家作为合作伙伴。Um, and so I think our customers are ultimately looking for a long-term partner that can do the robots, right? If one of our customers is manufacturing deodorant, they're doing the deodorant, right? And they're going to be operational experts in deodorant or operational experts in crayons or whatever the thing they manufacture is, and they need a partner who's the expert on robots.

工厂、中国与美国供应链Factories, China and the US supply chain

我有个关于工厂本质的问题。我的意思是，你可以随时把机器人运给任何人——今天还不行，因为你们有供应短缺，但未来某个时候就可以了，对吧？如果随着时间推移，你们的机器人变得越来越通用，这对工厂的选址，以及人们围绕这些工厂组织起来的方式意味着什么？还有，你知道的，这种“一座城镇一家工厂”——不管该怎么说——会发生很多根本性的变化，对吧？是的。呃，我觉得会有很多变化，对吧？I had a question on the the nature of factories and I mean you can ship a robot to anybody any time not today because of your shortages but at some point in the future right and if your robots become more and more general purpose over time what does it mean for where factories are located and how people organize around these factories and and you know these one town factories and what one factory town whatever the expression is a lot of fundamentally changes now, right? Yeah. Uh I think a lot changes, right?

这里面涉及很多地缘政治因素。我认为有件大事是，这将成为美国与其他国家之间的一种重要平衡力量。比如说中国，抱歉，是其他国家——它们曾投资于上一代工业自动化体系。在某些方面，这让它们非常成功；但从另一些方面来看，事后看来，这最终可能会成为一个巨大的错误。因为正如我们刚才讨论的，在美国，我们正从零开始建设，利用通用能力较强的机器人实现工业产能自动化；而在中国，他们将不得不拆掉现有设施，对吧？在美国，我们不必以同样的方式这么做。There's a lot of geopolitics in this. I think one big thing is this is going to be a great equalizer between the United States and other company or excuse me, other countries like China invested in the previous regime of industrial automation. And in some ways that made them very successful and in other ways I think in hindsight that might end up being an enormous mistake because in the United States we just discussed we're doing this green field build of automating our industrial capacity using generally capable robots in China they're going to have to tear down right we're not going to have to do that in the United States in the same way.

我认为另一个将发生转变、而且会非常有意思的方面是，工厂的数量会大幅增加。就像云计算和各种软件工具让人们得以创办新企业，从而使企业数量大幅增加一样，任何人都将能够在现实世界中创办企业。I think another thing that's going to shift which is going to be really interesting is we're just going to have way more factories. In the same way that we have way more businesses because kind of the cloud and all of the software tooling let people build new businesses. Anybody will be able to start a business in the physical world.

只要我们拥有这种无限且灵活的劳动力基础，任何人都能在现实世界中扩大业务规模。你刚才提到了中国。我们其实发布过一份关于机器人供应链的报告，报告似乎显示中国在其中处于核心地位。能不能谈谈你们对中国的依赖，以及这种依赖会如何随时间演变？中国会给供应链带来多大风险？Anybody will be able to scale a business in the physical world. if we kind of have this infinite flexible labor substrate. You you brought up China our you know uh you know we actually we actually published a report on the supply chain in robotics and it seemed to be that that China was so central to it. So can you talk a little bit about your dependency on it and then how that evolves over time and how much of there is there risk in China to the supply chain.

是的，呃，我觉得说到底，不管今天是 2026 年谁说了什么，我们都有一条高度互联、相互依存的全球供应链，而这只是众多例子之一。我认为，美国公司和中国公司都在认真思考，这件事的长期战略究竟应该是什么：既有中国公司投资美国，也有美国公司投资中国，而最终大家都在努力提升自己的产业能力。是的，我注意到，据我所知，你们已经做得更深入了，进行了更多垂直整合。Yeah. Uh I think ultimately no matter what anybody says today in 2026 we have a highly interconnected interdependent global supply chain and this is one of many examples of this and I think both US companies and Chinese companies are thinking very hard about kind of what does the long-term strategy of this look like and it's Chinese companies investing in United States it's United States companies investing China and ultimately everybody trying to build their industrial capacity. Yeah, I've I've noticed that you know you've gotten as far as I can um tell you've um verticalized more.

是这样吗？比如说，你们正在把更多业务收归内部，而且会继续——你们计划继续这么做吗？是的。Is that right? Like you're taking more in house and do you continue do you plan to continue to do so? Yeah.

嗯，供应链里有件棘手的事，而且由于机器人行业很像一个小作坊式产业，你一开始可能处在这样一种情况：零部件来自 OEM。然后你的产量不断增长，最后他们就不再只是 OEM 了，而是名副其实的合同制造商，对吧？他们的业务量中有相当大一部分都来自你。到了这一步，关系和合作模式就会发生变化，对吧？比如，这种产品该找这家共同制造商合作吗？我想用共同制造商吗，还是想自己在内部生产？是啊，我是说，Tutor 肯定是美国部署机器人最多的公司之一吧？所以你肯定也是某些 OVM 最大的客户之一。这样一来，我想随着时间推移，你会继续推动这些零部件由自己来做。我觉得是这样。那这意味着把生产收归内部，还是只是告诉他们该怎么做？我是说，他们还是供应商吗？Well, there's this tough thing in supply chain where and again because robotics is such a cottage industry is you might start um in a world where subcomponents are coming from OEMs and then you grow your volume to the point of they're not OEMs anymore. They're they're glorified contract manufacturers, right? ones are doing a material percentage of their volume and at that point just the relationship and the dynamics change right it's like is that the right co-man for that thing do I want to be using a co-and or do I want to be manufacturing it in house yeah I mean tutor must be one of the largest deployers of robots in the United States you know as a result you must be like one of the largest customers for some of these OVMs you know as a result I would imagine over time like you just continue to like drive you know these components yourself I think that's right does that mean taking it in house or just telling them what to do I mean is it like are they still suppliers?

我想，某种组合，大概就是某种组合吧。我认为，归根结底，要让 Tutor 取得成功，我们需要在美国建立成熟的机器人供应链。我们如今处于这样一种境地：我们本身就占了这条供应链很大一部分需求。我认为，投资建设美国的这项产能，是我们的责任，也是我们的机遇，而且我们需要投入非常、非常、非常多。Some combination I guess is it some combination. I think ultimately for tutor to be successful we will need a mature robotic supply chain in the United States and we're in this position where because we are so much of the demand for that supply chain. I think it's both our obligation and our opportunity to invest very very very heavily to building this capacity in United States.

这就是我们在马萨诸塞州沃特敦制造机器人的原因。这也是我们预计将继续垂直整合，并在美国制造越来越多机器人组件的原因。And that's why we build robots in Watertown Massachusetts. It's why we expect to continue to verticalize and build more and more of our robot componentry in the United States.

大规模真实世界强化学习Real-world reinforcement learning at scale

再回到我们刚开始交谈的时候，我记得我们真正开始聊研究，大概是在第二次会面时，对吧？那次会面的大部分时间都在聊你的生意做得有多好——这也是你的一大优势，对吧？然后我们还谈了你是如何着手的，以及关于哪些机器人学习模型会胜出之类的各种理论。Again, going back to when we first started talking, I remember it was probably the second meeting when we actually started about the research, right? Most of the meeting because you got such a great business, but that's a big strength of yours, right? And talk about how you approached it and there's all sorts of theories about which robotic learning models will win and so on so forth.

你们是如何开展机器人学习这一块的？嗯，在研究中，我觉得我们正在思考这件事在大语言模型领域是如何发展的，以及哪些方面相似、哪些方面不同。我认为，大语言模型领域有一件事显然非常重要：数据来源有很多种，彼此也各不相同，但归根结底，数据金字塔的顶端是现实世界中的强化学习轨迹，也就是实际执行你希望模型能够完成的任务所产生的轨迹。How how are you approaching the robot the learning bed? Yeah, in research I think we're thinking about how this played out in LLMs and what's similar and what's different. And I think one thing that clearly has been very very important in LLM is there are lots of heterogeneous sources of data but ultimately there's a top of the data period which is real world reinforcement learning traces doing the thing you want to be able to do.

因此，我们构建研究计划时，着眼于如何最大限度地利用机器人在真实世界中的经验。嗯，所以我们在波士顿建成了一支由 100 台机器人组成的机器人群，名为 Data Factory 1。据我们所知，这是美国规模最大的半人形机器人数据采集场。And so we've built our research program in such a way where we are thinking about how do we get the most out of robot experience out in the real world. Um and so we built this 100 robot fleet called data factory 1 in Boston. This is to our knowledge the largest sort of semi-humanoid data collection farm in the United States.

我们可以把它当作一个研究试验平台，探索如何利用机器人在真实世界中大规模开展强化学习。你知道，强化学习是一类技术方法，在小批量数据下确实不太奏效。因此，如果你在真实世界中拥有一支规模庞大的机器人队伍，就可以开始尝试这些方法。我觉得，无论是在学术界还是工业界，人们之前都没有机会尝试这些方法。And we're able to use this as kind of this research test bed of how do we do real world reinforcement learning at scale with robots. Um, you know, reinforcement learning is a technique family where uh it doesn't really work at small batch sizes. Um, and so if you have a very large fleet out in the real world, you can start to play with these methods which I think people haven't been able to play with either in academia or industry.

所以，我我我猜，组建这样一支团队和组建实验室团队很不一样，对吧？能不能聊聊团队是怎么组建的，以及你们有没有在物色要招聘的人？我们什么人都想招。So I I I presume like building a a team for this is quite different, right, than like you know lab. So maybe talk about kind of like the construction of the team and maybe if there's anybody you're looking to to hire. We're looking to hire everybody.

所以我们正在招聘研究人员和工程师，涵盖整个技术栈的每一层。呃，我们也在招聘技术人员来制造机器人。顺便问一下，你说技术栈的每一层，是指软件、硬件、材料、电子这些方面吗？So we're looking to hire researchers and engineers and that's across every single layer of the stack. Uh we are also looking to hire uh technicians to build robots. By the way, every layer of the stack you mean like software, hardware, materials, electronics like there.

是啊。你没法逃避责任，对吧？不管你愿不愿意，都得面对它。Yeah. There's nowhere that you can abdicate responsibility, right? You're going to engage with it whether you want to or not.

问题在于你是否理解这一点。嗯，所以我们正在组建这样一支团队，我认为这支团队的形态非常独特，不仅因为它跨越技术栈的各个层面，具有很强的跨学科性和协作性，还因为我觉得，在整个世界上，只有少数地方能让你早上还在白板前讨论模型架构，下午就去工厂生产线上看看机器人运行得怎么样。你们主要是在马萨诸塞州吗？我们在美国各地都有团队，尤其是负责市场拓展的同事和技术人员，他们会与客户接洽。我们的客户遍布美国各地。The question is whether you understand it. Um so so we're we're building that team and I think that's a really unique shape of team not only because it's so interdisciplinary and collaborative across all those layers of the technical stack but because I think it's one of the only places in the universe where you can be on a whiteboard talking about model architecture in the morning in the afternoon like you know looking at a factory line and seeing how a robot is doing and is this primarily in Massachusetts or so we have a team across the United States um we have especially folks on the go to market and technicians who are kind of engaging you know our customers are everywhere across the United States.

很好。好吧。太棒了。That's good. All right. Awesome.

谢谢。Thank you.