
伦敦机器人初创公司 Machenta 完成 4000 万美元种子轮融资。该公司源自伦敦帝国理工学院。据帝国理工学院 10 月 27 日消息,Creandum 和 Union Square Ventures 共同领投。该校称这是英国机器人初创企业有史以来最大的种子轮融资。
Machenta, a London robotics startup spun out of Imperial College London, has raised a $40M seed round. It is building an AI model that teaches robots a new task from a single demonstration. Creandum and Union Square Ventures co-led the round, Imperial said on 27 October. The university calls it the largest seed round ever raised by a UK robotics startup.
Machenta 完全在模拟数据、计算机生成的环境中训练模型,而非使用人们工作的真实影像。Machenta 首席执行官兼联合创始人 Edward Johns 是帝国理工学院机器人学习教授。他告诉《商业内幕》记者 Rya Jetha,该模型不使用“任何一个像素的真实世界数据”。《商业内幕》报道,Machenta 拒绝透露估值。
Machenta trains its model entirely on simulated data, in computer-generated environments, rather than on footage of people at work. Edward Johns is Machenta’s CEO and co-founder, and a professor of robot learning at Imperial. He told Rya Jetha of Business Insider that the model uses no “single pixel of real-world data”. Machenta declined to disclose its valuation, BI reported.
“智力不在于你已经能做什么,而在于你能多快学会做新事物。人类不需要反复看同一件事才能学会做它,”Johns 在帝国理工学院的声明中说。
“Intelligence is not so much about what you can already do, it’s about how quickly you can learn to do something new. Humans don’t need to see the same thing over and over before they can do it,” Johns said in Imperial’s announcement.
Johns 告诉《商业内幕》,模型训练完成后,只需看一次演示,无需进一步训练,即可指挥机器人完成装箱、整理桌面等新任务。帝国理工学院表示,同一个模型已在包括人形机器人在内的不同机器人上学会了装箱和整理。
One demonstration Once trained, the model can direct a robot to do a new task, such as packing a box or tidying a table, Johns told BI. It needs to see the task done only once, with no further training. Imperial says one model has learned packing and tidying across different robots, including humanoids.
这项工作始于六年前 Johns 在帝国理工学院的机器人学习实验室。据《商业内幕》报道,早期实验中,机器人在被引导完成任务后,还需要约 30 分钟的额外数据采集,才能独自完成任务。Johns 和他的前博士生 Vitalis Vosylius 于去年底创立 Machenta,旨在省去这一步骤。
The work began six years ago in Johns’s Robot Learning Lab at Imperial. In early experiments, a robot guided through a task then needed about 30 minutes of extra data collection, BI reported. Only after that could it do the task alone. Johns and his former PhD student Vitalis Vosylius founded Machenta late last year to remove that step.
业界大多走相反路径,收集真实世界数据。上周,Maxinsights 融资 3000 万美元,用于支付承包商拍摄工作影像以训练机器人。
Much of the industry is going the other way and collecting real-world data. Last week, Maxinsights raised $30M to pay contractors to film their work for robot training.
投资人与竞争对手“极少有机器人团队能拿出这样一批成果,它反复改变了人们对可能性的认知,”Creandum 合伙人 Kathi Wilhelm 说。
Investors and rivals“Very few teams in robotics can point to a body of work like this, which has repeatedly changed what people thought was possible,” said Kathi Wilhelm, a partner at Creandum.
USV 管理合伙人 Rebecca Kaden 在一篇博客文章中写道,该团队将其方法称为“物理氛围编程”。
USV managing partner Rebecca Kaden wrote in a blog post that the team calls its approach “physical vibe coding”.
“有了 Machenta,当机器人变得快速且易于训练时,想象力而非能力成为了制约因素,” Kaden 写道。
“With Machenta, when robots are fast and easy to train, imagination instead of capability becomes the constraint,” Kaden wrote.
据《商业内幕》报道,Machenta 拥有 11 名员工,仍专注于研究。它没有近期出售其技术的计划。更精确的任务和复杂的动作对其模型而言仍然困难。
Machenta has 11 employees and remains focused on research, according to BI. It has no near-term plans to sell its technology. More precise tasks and complex movements remain difficult for its model.
其竞争对手规模大得多。《商业内幕》报道称,Physical Intelligence 估值约为 110 亿美元,FieldAI 正在按 100 亿美元的估值融资。Skild AI 估值超过 140 亿美元,已在销售类似技术。8 月,Skild 表示其新模型在观看一次视频演示后,能够完成持续长达 10 分钟的未见任务。该公司上个月表示,其年度经常性收入已达 1 亿美元。
Its rivals are far larger. Physical Intelligence is valued at about $11B, and FieldAI is raising money at a $10B valuation, BI reported. Skild AI, valued at over $14B, already sells similar technology. In August, Skild said its new model could do unseen tasks lasting up to 10 minutes after one video demonstration. It said last month that it had reached $100M annual recurring revenue.
Machenta 将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。它还将搬入位于伦敦国王十字区的新办公室和实验室。
Machenta will use the money to grow its team and to train its model with more computing power and simulated data. It is also moving into a new office and lab in King’s Cross, London.