Machenta, a frontier AI company building a foundation model for robots, has raised £30 million in seed funding co-led by Creandum and Union Square Ventures. The funding will be used to scale Machenta's instant learning model and expand its team at a new office and lab.
People learn new physical tasks quickly: show someone how to do something once and, in many cases, they can do it straight away. Robots today are different. Teaching them a new task usually takes days or weeks. Hundreds or thousands of human demonstrations are collected, the model is trained on that data, and only then can the robot perform the task by itself. Machenta has built a foundational model designed to close that gap and achieve instant learning. If a robot sees just one demonstration of a task, it can perform the task immediately afterwards, adapting to new variations without further training. Less than a year after Machenta came together, the team has already shown that the approach works across packaging, assembling and tidying.
Dominant approaches today require huge amounts of real-world data, which has led to a data collection race such as paying people to remotely teleoperate robots or to record themselves at home doing everyday tasks. Stanford's 2026 AI Index identifies data as the biggest constraint facing Physical AI. Machenta takes the opposite route. To induce instant learning, the team trains the model entirely in simulation, so it can learn at huge scale across a more diverse set of scenarios than humans could collect physically. The goal is not to learn every task a robot may one day encounter, but to let a robot learn how to learn, so any new task is truly instant after a single example.
The work behind Machenta began six years ago at Imperial College London, where Edward Johns asked whether a robot could learn new physical skills as quickly as a human. Johns is Professor of Robot Learning at Imperial, and with his PhD student Vitalis Vosylius he built the foundations of in-context learning in robotics. After early signs that this intelligence was possible in the physical world, Johns and Vosylius founded Machenta to scale the work, bringing Johns's PhD team from Imperial with them. They have recently shown how a single model can instantly learn everyday tasks across any robot, including humanoids, and won the Best Paper Award at the ICLR 2025 Robot Learning Workshop.
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. If you take some objects, and show somebody what to do with them, they'll probably then be able to do the same task immediately. We've spent years trying to understand what it would take for robots to learn at the speed of a human. That is what instant robot learning is about.
Very few teams in robotics can point to a body of work like this, which has repeatedly changed what people thought was possible. The team at Machenta has spent years breaking through this extremely hard problem, long before there was a company around it. They're not starting with just a theory of how robots can learn like humans; they're building on years of experience actually making it happen.
Robots become so much more useful when they can instantly pick up a new task and get to work right away. The industry is pouring enormous amounts of capital into collecting real-world data and training robots one task at a time, but Machenta is changing this. Whether this is for a robot in the factory or at home, we need general-purpose AI which can adapt quickly in the same way that humans can.
