Reimagine Robotics Emerges from Stealth with Robots That Learn from Workers on the Job

Traditional industrial robots typically require engineers or integrators to reprogram them when processes change, adding time and cost and limiting automation to repetitive, highly structured tasks. Reimagine Robotics' founders believe that useful robots should learn from the people doing the work: workers can demonstrate tasks, correct errors, and then move on to the next problem, a process they call 'monkey-see, monkey-do.'

The core idea is to make automation adaptable to low-volume or high-variability processes, scenarios that are often difficult to justify with traditional automation. The company claims its system is already running in manufacturing and electronics disassembly, but specific deployment scale has not been disclosed.

Co-founder and CEO Jonathan Scholz previously led Google DeepMind's applied robotics team in London for seven years before co-founding Reimagine Robotics in April 2025 with colleagues Oleg Sushkov, Akhil Raju, and Misha Denil. The company has dual headquarters in London and Sydney.

This background provides some support for the company's technical credibility, but the information is based solely on the company's press release and has not been independently verified.

The company says initial development was supported by pre-seed funding from Fly Ventures, firstminute capital, and angel investors. It is now seeking a new round of funding to expand its team, increase deployments, and prove that experience from each installation makes subsequent projects faster and more reliable.

The specific amounts and valuations of the funding were not disclosed in the source, so the financial scale cannot be assessed.

At a custom plastics manufacturer, Reimagine Robotics trained robots to operate 3D printers overnight, including removing print beds, operating latches, and pressing controls. Customer employees then used the same platform to automate additional steps such as washing, curing, and drying, showing that workers can expand automation themselves without waiting for external experts.

In another project, the company worked with process engineers to create a three-robot system for disassembling used hard drives and recovering valuable materials. Robots and employees worked together, adjusting and improving the workflow. The company claims that in this project, the time to develop new robot behaviors was reduced from about a day to about 10 minutes.

These cases come from the company's own descriptions and lack third-party verification, so they should be viewed as company claims rather than confirmed facts.

Scholz emphasizes that this technology does not remove people from the process but relies on humans to identify bottlenecks, demonstrate tasks, and correct robots until they are useful. This positioning may help alleviate concerns about automation replacing jobs, but the actual effect remains to be seen.

If the technology delivers on its promises, it could lower the barrier to industrial automation, enabling small and medium-sized enterprises to benefit from robots. However, current evidence is limited, and its long-term impact is uncertain.