Strive Robotics Raises $3 Million to Teach Factory Robots by Demonstration

Boston startup Strive Robotics raised a $3 million pre-seed round led by Hi Ventures to build robots that learn factory workflows from human demonstration instead of fixed programming.

Key Takeaways

  • Strive Robotics raised a $3 million pre-seed round led by Hi Ventures, announced November 5, 2026.
  • Founders Craig Cheney, Geehoon Park and Paul Guiloff are all MIT alumni based in Boston.
  • The company's Apprentice Mode lets robots learn factory tasks by watching human operators rather than being programmed for each job.
  • Strive targets high-mix manufacturing, where frequent production changes make fixed automation uneconomical.
  • The startup is running fieldwork with an unnamed high-mix manufacturer, using real parts and equipment.

Strive Robotics Raises $3 Million to Teach Factory Robots by Demonstration

Strive Robotics raised more than $3 million in pre-seed funding announced November 5, led by Hi Ventures with participation from Grid Capital, Tenon Ventures and Thin Capital. The Boston-based startup, founded by MIT alumni Craig Cheney, Geehoon Park and Paul Guiloff, builds robots aimed at factories where production changes too often for traditional fixed automation to pay off.

The company's core pitch is a feature it calls Apprentice Mode, which lets a robot learn a task by watching a human operator perform it rather than requiring custom programming for every new part or process. The system is designed to operate existing factory controls — buttons, doors, machine interfaces — and to handle full workflows such as loading parts and verifying finished results against work instructions and CAD data.

Targeting the gap fixed automation can't fill

Strive is chasing what the industry calls high-mix manufacturing: production lines that switch between many different parts or short runs, where the cost of integrating a conventional robot arm often exceeds the value of automating the task. Co-founder Paul Guiloff said the next generation of industrial robots \"needs to understand and execute the broader workflow, adapt as production changes,\" framing the company's bet against single-task robot arms sold by larger industrial automation vendors.

That positioning puts Strive in a crowded field of startups promising to cut the system-integration step out of factory robotics, including companies pursuing similar learning-by-demonstration approaches for warehouse and assembly tasks. The $3 million round is small relative to recent raises elsewhere in physical AI — rivals building general-purpose industrial robot arms have raised rounds in the tens of millions this year — which suggests Strive is betting on an early, narrow deployment to prove the approach before seeking larger capital.

Next steps on the factory floor

Strive says it is already running fieldwork with a high-mix manufacturer, using real parts and equipment to test whether Apprentice Mode can sustain operation beyond a demo. The company has not named the manufacturer or disclosed revenue. The new capital will go toward product development, hiring and additional factory deployments as Strive tries to move from prototype trials to paid, sustained use — the step where many robot-learning startups have stalled in the past.

Category: Funding & Investments

Tags: Industrial Robots Physical AI pre-seed funding Strive Robotics high-mix manufacturing

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Frequently Asked Questions

How much funding did Strive Robotics raise?

Strive Robotics raised more than $3 million in a pre-seed round led by Hi Ventures, with Grid Capital, Tenon Ventures and Thin Capital also participating.

What is Strive Robotics' Apprentice Mode?

Apprentice Mode is a feature that lets Strive's robots learn a factory task by observing a human operator perform it, rather than being manually programmed for each new part or process.

Who founded Strive Robotics?

Strive Robotics was founded by MIT alumni Craig Cheney, Geehoon Park and Paul Guiloff, and the company is based in Boston.

What problem is Strive Robotics trying to solve?

It targets high-mix manufacturing environments where production requirements change often enough that traditional fixed robot automation is not economically viable.