Machenta raises £30m led by Creandum and Union Square Ventures
This article covers Machenta, a startup that has raised £30m in a seed funding round co-led by Creandum and Union Square Ventures to scale a foundation model enabling robots to learn new physical tasks from a single demonstration. The funding will be used to grow the team, open a new office and lab, and develop one-shot "instant learning" for robots, a capability that could affect deployment in factories, warehouses and homes.
Machenta has raised £30 million in a seed funding round co-led by Creandum and Union Square Ventures to scale a foundation model that enables robots to learn new physical tasks from a single demonstration. The cash will fund growth of the team, a new office and lab, and further development of what the company describes as “instant learning” for robots — a capability that, if realised at scale, could change how robots are deployed in factories, warehouses and homes.
Why it matters
Robots today typically require large datasets and lengthy training to acquire new skills. That means slow, task-by-task deployment and high costs for data collection. Machenta’s approach aims to let robots generalise from one example, cutting the time from demonstration to usable behaviour from days or weeks to minutes. If robust in real-world settings, this could reduce the barrier to automating niche or changing tasks and shift investment away from ever-larger datasets.
The announcement also highlights a broader tension in robotics research: Stanford’s 2026 AI Index flags data as the biggest constraint facing physical AI, and many companies are responding by funding extensive real-world collection efforts. Machenta’s simulation-first method offers a contrasting route, which may appeal to machine learning investors looking for alternatives to the current data-collection race.
Inside the product
Machenta’s core idea is to train a single foundational model entirely in simulation so it learns how to learn, rather than memorising specific tasks. The model is designed to perform a new task after seeing just one demonstration and to adapt to variations without further training. Early demonstrations reported by the company cover packaging, assembling and tidying tasks and, according to Machenta, the same model can be applied across different robot bodies, including humanoid platforms.
The team argues simulation enables exposure to a far larger and more diverse set of scenarios than practical real-world collection allows, letting the model internalise the learning process itself. The work began as academic research at Imperial College London and the group has published results that earned the Best Paper Award at the ICLR 2025 Robot Learning Workshop.
Who are the investors?
Machenta’s £30 million seed was co-led by Creandum and Union Square Ventures. The round will fund scaling of the company’s instant learning model, the establishment of a new office and lab, and hiring to grow the team brought over from Imperial College London.
In the announcement, Kathi Wilhelm, Partner at Creandum, said:
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.
In the announcement, Rebecca Kaden, Managing Partner at Union Square Ventures, said:
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.
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Founder perspective
Edward Johns and Vitalis Vosylius founded Machenta out of research at Imperial College London; Johns is Professor of Robot Learning at Imperial. The company formed less than a year ago after early academic work suggested in-context learning could transfer to the physical world. That transition from lab results to a company focus is central to Machenta’s claim that they are already demonstrating one-shot learning across multiple tasks.
In the announcement, Edward Johns, Co-founder & CEO at Machenta, said:
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.
The bigger picture
Machenta’s seed round sits at the intersection of academic robotics and venture-backed productisation. The company is betting that simulation-driven, generalist models can outperform the prevailing strategy of collecting more and more real-world demonstrations. For manufacturers and logistics operators, a genuinely one-shot learning robot could lower integration costs and speed deployment of automation for variable tasks.
The funding also reflects continued interest from transatlantic investors in UK robotics and AI spinouts: Creandum brings European venture capital experience while Union Square Ventures signals US investor appetite. As the UK seeks to convert strong robotics research into companies, Machenta is an example of academic work moving quickly into the commercial arena — a trend policymakers and investors are watching as the region competes to lead in applied AI and robotics innovation.
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