Machenta, a startup developing foundation models for robotics, has secured a £30 million seed round co-led by Creandum and Union Square Ventures. The London-based company focuses on teaching robots to learn new physical tasks at the speed of humans.
Instant learning versus data collection
Humans typically master a new physical task after a single demonstration. Modern robots require significantly more effort. Training a machine on a new task usually takes several days or weeks. The current industry standard involves collecting hundreds or thousands of human demonstrations before a model can perform the action. This laborious process involves first gathering data, then training the model, and finally waiting for the robot to perform the task autonomously after a lengthy delay.
Origins at Imperial College London
The work began six years ago at Imperial College London. Professor Edward Johns, who co-founded Machenta, focused on a question that was largely outside mainstream robotics research. He asked whether a robot could learn new physical skills as quickly as a human.
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Accessibility and future applications
The funding will scale the instant learning model and expand the team in a new office and lab in King’s Cross, London. Partners at the lead investors highlighted the team’s track record. Kathi Wilhelm at Creandum noted that few teams can point to work that repeatedly changes perceived possibilities.
Rebecca Kaden at Union Square Ventures added that robots become far more useful when they can adapt quickly. “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, partner at Creandum. “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 conclusion, with the new funding, Machenta is poised to further develop its instant learning model, making robots more accessible and useful in various settings.
