内容来源：https://techcrunch.com/2026/07/26/are-brain-waves-the-next-unlock-for-physical-ai/

内容总结：

前沿追踪：为训练机器人，加州仓库里的“积木游戏”正融入脑电波技术

在加利福尼亚州圣莱安德罗的一个仓库里，一场别开生面的“叠叠乐”（Jenga）游戏正在上演。这里是AI数据工具公司Encord的驻地，员工安德鲁·塞哈（Andrew Ceja）头戴装有摄像头的耳机，小心翼翼地抽取积木。这套装备不仅能记录视觉数据，还配备了德国神经科学初创公司Zander Labs的脑电波传感器，用于测量他拆解积木时的脑部活动。

这并非单纯的娱乐，而是物理AI领域解决数据短缺的前沿尝试。Encord与Zander Labs合作，试图通过捕捉人类在执行任务时的“错误、意图与惊讶”等心理状态，生成比传统数据更优质的训练集，以突破人形机器人和仓储机器人的发展瓶颈。Encord机器人学习主管维尼思·维尔穆鲁甘（Vineeth Velmurugan）指出，目前物理AI面临的最大限制并非模型架构，而是真实世界训练数据的极度匮乏。他曾是OpenAI机器人实验室和仓储自动化公司Berkshire Grey的资深成员，如今正带领Encord内部团队专门“制造”数据。

数据从何而来？从“第三人称”到“第一人称”

为解决这一瓶颈，业界正转向两大来源：一是工人佩戴相机采集的“第一人称”视频（通常辅以多角度摄像头与指标），二是通过远程操控机器人收集数据。Encord双管齐下，既在全球多个工厂采集第一人称数据，也在其圣莱安德罗设施中实验脑电波、前臂肌电信号等新模式。

在仓库的另一角，员工索菲亚·因凡特（Sofia Infante）正操作一对“主从式”机械臂，练习给服务器插拔以太网线。这种数据中心希望自动化的精细操作，对人形机器人而言仍是巨大挑战。此外，仓库货架上摆满了假花、塑料蔬菜、猫砂盆等杂物，这些是训练机器人完成家务操作的重要道具。

数据质量决定性价比：精细标注虽贵但值

Encord不仅收集数据，还为视频添加“右手拧紧螺栓”等物理描述性文字标签，以帮助大语言模型理解动作。维尔穆鲁甘表示，这种“密集标注”数据在训练特定任务时价值是普通“第一人称垃圾数据”的100倍，而生产成本仅为后者的20倍——理论上是一笔划算的交易。但“20倍”仍是不小的成本，这与聊天机器人轻松抓取互联网文本不同，物理训练数据必须“制造”而非简单“收集”，这从根本上改变了AI模型的建设经济学。

行业“中立方”的独特视角

作为多家领先机器人公司的数据服务商（因保密协议不便透露客户名称），Encord认为自身处于跨行业观察的有利位置。维尔穆鲁甘透露，他们能比任何单一客户更早发现哪些数据技术在行业中取得进展。他估计，要取得突破性进展，可能需要相当于YouTube视频语料库五倍规模的数据集——这也解释了为何数据制造本身已从研究课题演变为一门生意。

目前，Encord仓库里有十多名像塞哈和因凡特这样的“飞行员”（pilot），他们曾是另一家AI数据标注公司Scale的员工。塞哈此前在废物管理公司负责维护机器人分拣器，如今他每天面对新的训练任务，乐在其中：“每天都有新挑战！” —— 从堆叠积木到插拔线缆，这些看似简单的动作，正在为物理AI的未来铺路。

中文翻译：

物理人工智能的前沿，是一场位于加州圣莱安德罗一座仓库里的叠叠乐游戏。

这座仓库属于Encord公司，这家公司致力于构建用于训练AI模型的数据工具。安德鲁·塞哈是一名“领航员”——这是该公司对其机器人训练员的称呼——他正戴着一副带有摄像头的头显，小心翼翼地从一个摇摇欲坠的塔中抽出木块，摄像头会追踪他看到的景象。仅就收集机器人训练数据而言，这相当常见，但这副头显还包含传感器，能在他小心拆解积木塔时测量他的脑电波。

Encord是一家为数不多、但数量正在增长的初创公司之一，它们押注人形机器人和仓储机器人领域的下一个真正瓶颈将不再是模型架构，而是现实世界物理训练数据的极度匮乏。Encord不仅帮助机器人公司管理他们已有的数据，更围绕制造他们所缺失的数据来构建业务。

塞哈佩戴的脑电波头显由德国神经科学初创公司Zander Labs制造，该公司押注通过测量大脑活动——推断出错误、意图和惊讶等心理状态——可以创建更有用的数据集来训练模型。Encord与Zander的合作目前是试用阶段；Encord表示，目标是在决定是否扩大规模之前，先构建一个初步的脑电波标记数据集，通过客户的机器人模型运行，并评估它是否真的能提升性能。

负责监督这项工作的Zander神经科学家卢卡斯·格尔克表示，在给定任务的任何时间点所使用的大脑活动量，为试图确定何时需要部署最高强度模型的模型构建者提供了线索。

据Encord机器人学习主管维内斯·韦尔穆鲁甘称，这是解决机器人数据瓶颈的“前沿尝试”。作为OpenAI机器人实验室和仓储自动化公司Berkshire Grey的资深人士，韦尔穆鲁甘加入Encord是为了组建公司内部的“数据创造”团队。

Encord成立的初衷是帮助构建机器视觉应用的公司注释数据和评估模型。随着他们的客户——韦尔穆鲁甘表示他们与许多顶级机器人公司合作，但未被授权透露其名称——开始将端到端学习应用于机器人操作任务，公司高管们意识到，他们必须自己生成训练数据，而不仅仅是管理数据。“这些数据根本不存在，”韦尔穆鲁甘说。

人们认为生成式AI能为机器人带来类似其对聊天机器人带来的变革，这一赌注不断撞上同一堵墙。大型语言模型建立在整个互联网的文本乃至更多资料之上。为神经网络找到同样的原始材料来教授其物理操作极具挑战性：自动驾驶公司自己收集数据，但这难以规模化。从视频中训练可能有效，但它缺乏真实世界数据的保真度。韦尔穆鲁甘表示，要取得突破，可能需要一个约为YouTube视频库规模五倍的数据集——这种规模有助于解释为什么数据生成本身已变成一项业务，而不仅仅是一个研究问题。

满足你的以自我为中心的数据需求

构建“机器人大脑”的公司现在正转向两个主要来源：由佩戴摄像头的工人收集的“以自我为中心”的视频（通常辅以额外的摄像机角度和其他指标），以及从远程操作的机器人处收集数据。Encord两者兼顾，从全球多个工厂获取以自我为中心的数据，并利用其圣莱安德罗的设施来试验新模式（如脑电波），或为微调而围绕特定技能收集数据集。

当TechCrunch访问时，“领航员”们正在使用“主从式”装置——一对机械臂，一个由人类操作员直接控制，另一个模仿其动作——来创建关于倒咖啡（非常容易泼溅）和叠筹码等任务的数据。“每个人形机器人公司都向我们索要这些数据，”韦尔穆鲁甘说。

存储架上放着花瓶中的人造花束、书籍、塑料蔬菜、猫砂盆和铲子、成袋成捆的电线——这些都是用于训练家用操作机器人的常用物品。

在其中一处工位，另一位“领航员”索菲亚·因凡特正在操控机械臂，从服务器背面插拔以太网线——数据中心运营商非常希望这类工作能实现自动化，只要机器人能具备所需的精度来操作它们。我亲自操作体验了一把，便明白了为何这仍遥不可及：机械钳的灵巧度远不及人类手指，并且缺乏我们在自己手臂中习以为常的自由度。

Encord正在开发的另一种新数据模式，使用一组绑在前臂上的传感器来检测肌肉中的电信号。人类手部操作物体的视频通常无法捕捉整个手部，但韦尔穆鲁甘希望基于手臂传感器构建出手部在任何时刻位置的3D描绘，从而为模型创建更全面的理解。

Encord的数据集都带有对每个视频内容的物理描述注释——例如“右手拧紧螺栓”——以帮助基于大型语言模型的模型理解正在发生什么。韦尔穆鲁甘估计，在训练特定任务方面，这种密集注释的价值是“劣质的以自我为中心的数据”的100倍，而其生成成本仅高出20倍，从理论上看，这是一笔划算的交易。

但“高出20倍”仍然是真金白银，这就是症结所在：从互联网上抓取文本——就像大型语言模型制造商通过从Stack Overflow和整个网络获取数据来构建模型那样——对前沿实验室来说几乎零成本。但生成物理训练数据则不然，这正是将物理AI与大型语言模型进行比较的局限所在。这类数据必须被“制造”，而不仅仅是“收集”，这改变了构建这些模型的经济学。

韦尔穆鲁甘表示，进展正在取得——凭借Encord对行业内各种项目的洞察力，他可以看到初创公司和前沿实验室都在摸索什么方法有效、什么无效，以改进物理AI模型。这种同时服务于多家机器人公司的独特视角，也是Encord卖点的一部分。它能在任何一个单一客户发现之前，就识别出哪些数据技术正在整个行业获得关注。

这将使Encord工厂里的十几名“领航员”忙碌起来。因凡特和塞哈都是新兴劳动力大军中的一员，致力于开发神经网络的基石；在加入Encord之前，他们曾在另一家AI数据标注公司Scale工作。

塞哈曾在一家废物管理公司工作，对技术的兴趣让他负责维护一台机器人垃圾分拣机的良好运行。如今，当叠叠乐塔倒塌时，他说他很享受解决机器人训练任务带来的挑战——“每天都有新花样！”

英文来源：

The frontier of physical AI is a Jenga game in a warehouse in San Leandro, California.
That warehouse is occupied by Encord, a company that builds data tooling used to train AI models. Andrew Ceja is a pilot—the company’s term for its robotic trainers—and he’s carefully pulling wooden blocks from a tottering tower while wearing a headset with a camera that tracks what he sees. That alone is fairly common for collecting robot training data, but this headset includes sensors that measure his brain waves as he carefully disassembles the block tower.
Encord is one of a small but growing number of startups betting that the next real constraint on humanoid and warehouse robotics won’t be model architecture but instead the sheer scarcity of real-world physical training data. Rather than just helping robotics companies manage the data they have, Encord is building a business around manufacturing the data they don’t.
The brain wave headset Ceja is wearing was built by Zander Labs, a German neuroscience startup that’s betting measuring brain activity — to deduce mental states like error, intent and surprise — can create a more useful data set to train models. Encord’s work with Zander is currently a trial run; Encord says the goal is to build an initial brain wave-tagged data set, run it through customer robotics models, and evaluate whether it actually improves performance before deciding whether to scale it up.
Lucas Gehrke, a Zander neuroscientist supervising the work, says that the amount of brain activity used at any point during a given task offers clues for model builders trying to figure out when they need to deploy their highest-effort models.
This is the “bleeding edge” of the effort to solve the robotics data bottleneck, according to Vineeth Velmurugan, Encord’s head of robot learning. A veteran of OpenAI’s robot lab and Berkshire Grey, the warehouse automation firm, Velmurugan joined Encord to build the company’s internal data-creation team.
Encord was founded to help companies building machine-vision applications annotate data and evaluate models. As their customers—Velmurugan says they work with many leading robotics firms but that he’s not authorized to name them—began to apply end-to-end learning to robotic manipulation tasks, executives realized they would have to produce training data themselves, rather than simply manage it. “The data simply does not exist,” Velmurugan said.
The bet that generative AI can do for robots what it’s done for chatbots keeps running into this same wall. LLMs were built on the text of the entire internet, and more. Finding the same raw materials to teach neural networks about physical manipulation is challenging: self-driving car companies collect it themselves, but that’s hard to scale. Training from video can work, but it lacks the fidelity of real world data. Velmurugan says it will take a data set something like five times the size of YouTube’s video corpus to break through—a scale that helps explain why data-generation itself has become a business and not just a research problem.
Feed your egocentric data needs
Companies building robot brains are now turning to two main sources: “Egocentric” video collected by workers wearing cameras, often augmented with additional camera angles and other metrics, and collecting data from robots operated remotely. Encord does both, drawing egocentric data from several factories around the globe, and using its San Leandro facility to experiment with new modalities, like brain waves, or collect data sets around specific skills for fine-tuning.
When TechCrunch visited, pilots were using leader-follower rigs — paired robotic arms, one controlled directly by a human operator and one that mimics its movements —to create data about tasks like pouring coffee from a pot into mugs (very sloshy) and stacking poker chips. “Every humanoid company has asked us for these pieces,” Velmurugan says.
Storage racks held cartons of fake flowers in vases, books, plastic vegetables, kitty litter trays and scoops, bags and bundles of wires, the stock in trade for training manipulators for household tasks.
At one of these stations, another pilot, Sofia Infante, maneuvers robotic arms to plug and unplug ethernet cables from the back of a server—the kind of work data center operators would love to be automated, if only robots could manipulate them with the required precision. Taking a spin behind the controls, I was able to see why that’s still out of reach: Pincers are far less dextrous than human fingers and lack the degrees of freedom we take for granted in our arms.
Another new data modality that Encord is developing uses a set of sensors strapped to the forearm to detect electrical signals in muscles. Video taken of human hands manipulating objects typically doesn’t capture the entire hand, but Velmurugan hopes to build a 3D depiction of where the hand is at any time based on the arm sensors, creating a more robust understanding for models.
Encord’s data sets are annotated with physical descriptions of what each video contains—”right hand tightens bolt”—to aid LLM-based models in understanding what is happening. Velmurugan estimates this kind of dense annotation is worth 100 times as much as “junky ego data” for training specific tasks, and it only costs 20 times more to produce, which is a good trade, on paper.
But “20 times more” is still real money, and that’s the catch: scraping text off the internet, the way LLM makers built their models by pulling from Stack Overflow and the rest of the web, cost frontier labs next to nothing. Generating physical training data does not, and that’s the limit of the physical-AI-as-LLM comparison. This kind of data has to be manufactured, not just collected, and that changes the economics of building these models.
Velmurugan says that progress is being made—with Encord’s visibility into programs across the industry, he’s able to see start-ups and frontier labs alike figure out what works and what doesn’t to improve physical AI models. That vantage point—sitting between many robotics companies at once—is also part of Encord’s pitch. It can spot which data techniques are gaining traction industry-wide before any single customer can.
That will keep the dozen or so pilots at Encord’s facility busy. Both Infante and Ceja are part of a burgeoning workforce developing the building blocks for neural networks; they previously worked at Scale, another AI data annotation firm, before joining Encord.
Ceja had worked at a waste management company where his interest in technology found him in charge of keeping a robotic trash sorter in good working order. Now, as the Jenga tower topples, he says he enjoys the challenge of solving training tasks for robots —”It’s something new every day!”