Industrial Next wants factory robots to switch tasks in minutes, not weeks

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In most factories, automation is fixed: robots and machines are programmed for one specific task and product. Changing a line means reprogramming and physical retooling, or swapping out equipment, which can take weeks or months. San Francisco-based Industrial Next says it’s changing that.

在大多数工厂中，自动化是固定的：机器人和机器被编程用于执行特定的任务和生产特定的产品。改变生产线意味着需要重新编程、进行物理改造或更换设备，这可能需要数周甚至数月的时间。总部位于旧金山的 Industrial Next 公司表示，他们正在改变这一现状。

The startup, which presented today on the Startup Battlefield stage at TechCrunch Disrupt 2026, builds AI-driven, self-contained robotic stations powered by proprietary foundation models that connect vision, task context and motion. The company says this lets a production line be retooled in minutes rather than weeks without traditional programming.

这家初创公司今天在 TechCrunch Disrupt 2026 的“创业战场”（Startup Battlefield）舞台上进行了展示。他们构建了由人工智能驱动的独立机器人工作站，这些工作站由专有的基础模型提供支持，能够连接视觉、任务背景和动作。该公司表示，这使得生产线无需传统编程，即可在几分钟而非几周内完成改造。

The goal is to let manufacturers automate complex and delicate tasks that traditionally require human labor. The company announced on stage that it’s partnering with BMW Group Technology Office to evaluate Industrial Next’s AI-powered dexterous manipulation for manufacturing.

其目标是让制造商能够自动化那些传统上需要人工完成的复杂且精细的任务。该公司在舞台上宣布，正与宝马集团技术办公室合作，评估 Industrial Next 基于人工智能的灵巧操作技术在制造业中的应用。

Industrial Next was founded in 2021 by Lukas Pankau and Allen Pan, both of whom previously worked at Tesla. Pankau served as a lead electrical architect at Tesla before moving on to Waymo. Pan led Tesla’s autonomous factory efforts and later became a chief architect at autonomous driving company Neolix.

Industrial Next 由 Lukas Pankau 和 Allen Pan 于 2021 年创立，两人此前均在特斯拉工作。Pankau 在加入 Waymo 之前曾担任特斯拉的首席电气架构师。Pan 则领导了特斯拉的自动化工厂项目，后来成为自动驾驶公司新石器（Neolix）的首席架构师。

The pair came up with the idea for Industrial Next while working together at Tesla. Pankau’s first encounter with Pan was literally tripping over him as Pan was asleep on a factory catwalk after pulling an all-nighter. Although the two didn’t get along at first, they soon realized they shared an obsession with getting things done and had complementary skills.

两人在特斯拉共事时萌生了创办 Industrial Next 的想法。Pankau 与 Pan 的第一次相遇非常戏剧性：Pan 在熬夜工作后睡在工厂的走道上，Pankau 差点被他绊倒。尽管两人起初并不合拍，但他们很快意识到彼此都对完成任务有着执着的追求，且技能互补。

After years of watching cutting-edge vehicle and self-driving technology get held back by decades-old factory equipment, the founders realized that manufacturing needed to be rebuilt.

在目睹了尖端的汽车和自动驾驶技术多年来一直受限于几十年前的工厂设备后，这两位创始人意识到制造业需要重构。

“We had all these fancy things that we could do on cars. I could run machine learning algorithms to design a car for me. But I couldn’t figure out how to program a robot, and this was pretty common for engineers at Tesla,” Pankau said. “You had this massive divide, right? You had people who are really good at what they’re doing–designing cars and designing self-driving systems–and then you had them effectively stumped at the factory looking at this equipment from the 1980s. And so we started rethinking what this should look like using the technology that we have today and using everything that we know today.”

“我们可以在汽车上实现各种炫酷的功能。我可以运行机器学习算法来为我设计汽车。但我却搞不定如何给机器人编程，这在特斯拉的工程师中非常普遍，”Pankau 说道。“存在着巨大的鸿沟，对吧？一方面是那些在设计汽车和自动驾驶系统方面非常出色的人才，另一方面，他们却在工厂里对着 20 世纪 80 年代的设备束手无策。因此，我们开始利用当今的技术和我们所掌握的一切知识，重新思考这一切应该是什么样子。”

The two kept thinking about their frustrations with industrial equipment even after moving on to their next jobs. They stayed in touch, often reminiscing about the machines that never functioned the way they wanted, and eventually decided they could do better.

即使在换了新工作后，两人仍对工业设备的局限性耿耿于怀。他们保持着联系，经常回忆起那些从未按预期运行的机器，最终决定他们可以做得更好。

“I was at Waymo at the time, kind of bored out of my mind because I was missing that pace of Tesla,” Pankau said. Pan, he added, was in a similar position at Momenta, a developer of self-driving systems in China. Pan recalled their conclusion: “We’re like, we’re not getting any younger, and we’re still obviously passionate about it. We still keep complaining about industrial technology. Like, this is three years later, we should start the company.”

“当时我在 Waymo，因为怀念特斯拉那种快节奏，感到非常无聊，”Pankau 说。他补充道，Pan 在中国自动驾驶系统开发商 Momenta 也处于类似的情况。Pan 回忆起他们的结论：“我们当时想，我们不再年轻了，但显然依然对此充满热情。我们还在不断抱怨工业技术。这已经是三年后了，我们应该创办这家公司。”

Pankau notes that traditional industrial robots are typically programmed to perform highly specific, repeatable tasks. As a result, even a small change in the process or product can cause the robot to fail or trigger costly downtime, which forces manufacturers to simplify their products for repeatability.

Pankau 指出，传统的工业机器人通常被编程用于执行高度特定且可重复的任务。因此，流程或产品的微小变化都可能导致机器人故障或引发昂贵的停机时间，这迫使制造商为了可重复性而简化产品。

Making robots truly flexible presents a different challenge. They need enormous amounts of training data and, unlike AI models trained on the internet, robotics lacks the vast datasets needed to teach machines how to perform complex physical tasks.

让机器人真正灵活则带来了不同的挑战。它们需要海量的训练数据，而且与在互联网上训练的人工智能模型不同，机器人领域缺乏教导机器执行复杂物理任务所需的大规模数据集。

“We can make robots do all these cool things like dance around, because there’s data for that, but we can’t make them assemble high-precision things,” Pankau explained. “This is where we’ve made a lot of progress on using simulated environments to do these high-precision and high-dexterity tasks. The secret sauce for the robots that we have is that we have given them an understanding of the process in general, but we build that understanding using an extremely high-fidelity simulation engine that is close to the real dynamics to where we can let the robot do these kinds of high precision tasks almost without seeing the parts at all.”

“我们可以让机器人做各种很酷的事情，比如跳舞，因为有相关数据，但我们无法让它们组装高精度的东西，”Pankau 解释道。“这就是我们在利用模拟环境执行这些高精度和高灵活性任务方面取得重大进展的地方。我们机器人的秘诀在于，我们赋予了它们对整个流程的理解，而这种理解是通过一个极其高保真的模拟引擎构建的，该引擎非常接近真实的动力学，以至于我们可以让机器人在几乎看不到零件的情况下完成这些高精度任务。”

In other words, the robots practice in a highly detailed virtual world that closely mimics real-world physics. Industrial Next’s robotic stations can be reconfigured, combined, and redeployed across product lines. New tasks are taught through simulation and demonstration, rather than hard-coded scripts.

换句话说，机器人在一个高度详细、紧密模拟现实物理世界的虚拟世界中进行练习。Industrial Next 的机器人工作站可以在不同的产品线之间进行重新配置、组合和重新部署。新任务通过模拟和演示来教授，而不是通过硬编码的脚本。

Pankau explained that some of the startup’s workstations are already used in making one of the world’s most popular smartphones. They handle tasks that manufacturers had tried and failed to automate for years, such as threading a delicate flex cable and snapping a tiny connector cap into place. (He wasn’t at liberty to disclose the customer.)

Pankau 解释说，该初创公司的一些工作站已经用于制造世界上最受欢迎的智能手机之一。它们处理了制造商多年来尝试自动化却失败的任务，例如穿过精细的柔性电缆并将微小的连接器盖扣入到位。（他不便透露客户名称。）

He also described a “film peeling” station, where their AI-powered robots strip protective film off camera modules and buttons at or above human speed with 100% accuracy, even when moved to a new phone model and production line they have never seen.

他还描述了一个“撕膜”工作站，其人工智能驱动的机器人能以人类的速度或更快的速度，以 100% 的准确率剥离摄像头模块和按钮上的保护膜，即使在被转移到它们从未见过的全新手机型号和生产线上时也是如此。

“We started this company because that is the kind of performance that we wish we had at Tesla, where we were bypassing robot stations because we couldn’t program them effectively enough using traditional technologies,” Pankau said. “Now we want to take this to more complicated tasks than just building a phone. We ultimately want to take this to building a full car end-to-end without any human touching ever.”

“我们创办这家公司是因为这正是我们希望在特斯拉拥有的那种性能，当时我们不得不绕过机器人工作站，因为我们无法使用传统技术有效地对它们进行编程，”Pankau 说。“现在，我们希望将其应用于比制造手机更复杂的任务。我们的最终目标是实现全自动化的整车制造，全程无需任何人工干预。”

To check out more of the Startup Battlefield — along with a slate of established founders and VCs whose takes on the industry you won’t want to miss — join us at Disrupt, happening this week in downtown San Francisco.

想要了解更多关于“创业战场”的内容，以及不容错过的知名创始人和风险投资人对行业的见解，请参加本周在旧金山市中心举行的 Disrupt 大会。