总部位于伦敦的机器人初创公司 Machenta 正押注一种新路径：无需收集大量真实世界数据，即可教会机器人新技能。该公司由伦敦帝国理工学院的研究人员创立，已完成 4000 万美元种子轮融资，用于构建一种 AI 模型，据称该模型只需观看一次演示，就能让机器人学会新的物理任务。Machenta 独家向《商业内幕》透露了这一消息。风投机构 Creandum 和 Union Square Ventures 共同领投该轮融资。Machenta 拒绝披露估值。
A London-based robotics startup is betting it can teach robots new tricks without collecting the mountains of real-world data its much bigger rivals are chasing. Machenta, founded by researchers from Imperial College London, has raised a $40 million seed round to build an AI model that can teach robots new physical tasks after seeing them performed just once, the company exclusively told Business Insider. Venture capital firms Creandum and Union Square Ventures co-led the round. Machenta declined to disclose its valuation. While rivals building general-purpose AI models for robots are scrambling to amass real-world data, Machenta is taking a contrarian approach.
在竞争对手纷纷争夺真实世界数据以构建通用机器人 AI 模型之际，Machenta 采取了一种反向策略。首席执行官兼联合创始人、帝国理工学院教授 Edward Johns 表示，公司在计算机生成的环境中训练模型，这些环境模拟物体的运动与交互，“完全不使用任何真实世界数据的像素”。训练完成后，模型能指挥机器人在仅观看一次演示、且无需进一步训练的情况下，执行整理桌面、将物体放入箱子等新任务。
Edward Johns, the startup's CEO and cofounder, and an Imperial professor, said the company trains its model in computer-generated environments that mimic how objects move and interact, without using a "single pixel of real-world data." Once trained, the model can direct a robot to perform a new task, such as tidying a table or putting objects into a box, after watching it done just once, without further training, Johns said. In a video shared with Business Insider, three robots pack and close a box on an assembly line, a task Johns said they learned this way.
Machenta 向《商业内幕》分享的一段视频显示，三台机器人在流水线上完成打包并封箱，Johns 称它们就是通过这种方式学会该任务的。
The startup has 11 employees and remains focused on research, with no near-term plans to commercialize its technology.
这家初创公司目前有 11 名员工，仍专注于研究，暂无近期商业化计划。其模型虽能即时学会部分任务，但更精准的任务和复杂动作仍具挑战。Machenta 面临的竞争对手估值达数十亿美元，所在领域是机器人赛道中竞争最激烈、资金最充裕的板块之一。总部位于加州的竞争对手包括：估值约 110 亿美元的 Physical Intelligence、据本月《商业内幕》报道正在以 100 亿美元估值融资的 FieldAI，以及估值超 140 亿美元、已在销售具备类似能力技术的 Skild AI。
Its model can learn some tasks immediately, but more precise tasks and complex movements remain challenging. Machenta faces rivals with multibillion-dollar valuations in one of robotics' most competitive and well-funded fields. Its California-based competitors include Physical Intelligence, valued at roughly $11 billion, and FieldAI, which is raising funding at a $10 billion valuation, Business Insider reported this month. Another rival, Skild AI, which is valued at over $14 billion, is already selling technology with similar capabilities.
8月，Skild发布了一款模型，称其能在观看单次视频演示后，执行时长长达10分钟的此前未见过的任务。上个月，该公司表示，已从60多位客户那里实现了1亿美元的年度经常性收入。六年前，Johns在帝国理工学院的实验室里开始研究机器人是否能像人类一样快速学习新的物理任务。在实验室早期的实验中，研究人员亲自引导机器人完成任务，例如拿起铲子并铲起一个物体。
In August, Skild unveiled a model that it said could perform previously unseen tasks lasting up to 10 minutes after a single video demonstration. Last month, it said that it had reached $100 million in annual recurring revenue from more than 60 customers. Six years ago, Johns began investigating whether robots could learn new physical tasks as quickly as humans at his Imperial College lab. In the lab's early experiments, researchers physically guided a robot through a task, such as picking up a spatula and scooping an object.
随后，机器人会花费约30分钟收集关于该物体及其周围环境的额外数据，然后才能自主执行该任务。Johns和前博士生Vitalis Vosylius随后花费数年时间致力于消除这一额外步骤，在看到他们的模型在演示后能立即执行新任务的早期迹象后，于去年底创立了Machenta。企业正越来越多地购买和收集数据，以教导机器人如何与现实世界互动。
The robot then spent about 30 minutes collecting additional data about the object and its surroundings before it could perform the task on its own. Johns and former Ph.D. student Vitalis Vosylius spent the next few years working to eliminate that extra step, founding Machenta late last year after seeing early signs that their model could perform new tasks immediately after a demonstration. Companies are increasingly buying and collecting data to teach robots how to interact with the real world.
但Johns认为，单纯收集更多数据并非答案。要让通用机器人成为现实，究竟需要多少训练数据，目前仍不清楚。Johns表示，Machenta正在迎接更艰巨的挑战：设计能够生成合成数据的算法，这些数据可用于教导机器人完成物理任务。Machenta计划利用这笔资金扩充团队，并利用更多算力和模拟数据训练其模型，希望教会机器人执行更精确、更复杂的任务。
But Johns believes simply collecting more data isn't the answer. It also remains unclear how much training data will be needed to make general-purpose robots a reality. Johns said that Machenta is taking on the harder challenge of designing algorithms that can generate synthetic data useful for teaching robots physical tasks. Machenta plans to use the funding to expand its team and train its model with more computing power and simulated data, hoping to teach robots to perform more precise and complex tasks.