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. 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.
The startup has 11 employees and remains focused on research, with no near-term plans to commercialize its technology. 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. 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.
From research lab to startup
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. 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. 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.
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