If eight billion humans already possess the dexterous manipulation skills we want robots to have, why not let robots learn directly from humans, in realtime, at industrial scale and in the environments where they are needed? 

This central thesis for Tutor Intelligence drives its focus on getting its robots live in the field, working quickly, operating as a flexible workforce, and learning at rapid speed from both humans and models. Tutor began when cofounders Josh and Alon met as graduate students at MIT working on reinforcement learning for robotics. In a field obsessed with building smarter algorithms, they shared the same core belief that the hardware was already capable, it was the intelligence that lagged. The prevailing approaches, whether hand-coded heuristics or robots learning everything from scratch, broke down quickly in the real world. 

In Tutor’s system, a robot shows up at a customer site, plugs in, and begins working immediately. When it encounters something unfamiliar, it can call on a remote human “tutor” who takes control for a moment and generates the exact training data the model needs. Every deployment streams data back into a shared software stack, allowing the entire fleet to get smarter with each hour of operation. The gap between “the robot has never seen this” and “the robot can now handle this autonomously” collapses into minutes rather than months. Traditional automation, by contrast, is built through expensive custom engineering projects that take years and are only accessible to the world’s largest manufacturers.

From the beginning, Tutor rejected the lab-first, deploy-later mentality that has slowed so many robotics efforts. Their first robot went into a New Jersey factory packaging cosmetics. At first it was fully teleoperated; a few months of real production data later, it was running autonomously. That early decision to commercialize immediately created a structural advantage with real customers, real variability, and real data loops. It also created a culture of speed that remains core to how the team operates. Walking their floor feels less like a lab and more like a workshop where hardware, software, data operations, and customer support are tightly integrated around a single goal of building a generally capable, software-defined industrial workforce.

We have long believed in the power of compounding data loops, particularly when the data is difficult to obtain. In software, unique data has grown harder to defend, but in robotics, the opposite is true. Every robot Tutor deploys becomes another node in a network collecting valuable, hard-to-replicate data that continually strengthens the system. And by pricing robots by the hour of runtime instead of as capital expenditure, Tutor makes automation accessible to mid-sized manufacturers and family businesses that have never been able to automate before. Its as effective and affordable for an auto manufacturer as it is for the long tail of mom and pop small businesses. 

If eight billion humans already possess the dexterous manipulation skills we want robots to have, why not let robots learn directly from humans, in realtime, at industrial scale and in the environments where they are needed? 

This central thesis for Tutor Intelligence drives its focus on getting its robots live in the field, working quickly, operating as a flexible workforce, and learning at rapid speed from both humans and models. Tutor began when cofounders Josh and Alon met as graduate students at MIT working on reinforcement learning for robotics. In a field obsessed with building smarter algorithms, they shared the same core belief that the hardware was already capable, it was the intelligence that lagged. The prevailing approaches, whether hand-coded heuristics or robots learning everything from scratch, broke down quickly in the real world. 

In Tutor’s system, a robot shows up at a customer site, plugs in, and begins working immediately. When it encounters something unfamiliar, it can call on a remote human “tutor” who takes control for a moment and generates the exact training data the model needs. Every deployment streams data back into a shared software stack, allowing the entire fleet to get smarter with each hour of operation. The gap between “the robot has never seen this” and “the robot can now handle this autonomously” collapses into minutes rather than months. Traditional automation, by contrast, is built through expensive custom engineering projects that take years and are only accessible to the world’s largest manufacturers.

From the beginning, Tutor rejected the lab-first, deploy-later mentality that has slowed so many robotics efforts. Their first robot went into a New Jersey factory packaging cosmetics. At first it was fully teleoperated; a few months of real production data later, it was running autonomously. That early decision to commercialize immediately created a structural advantage with real customers, real variability, and real data loops. It also created a culture of speed that remains core to how the team operates. Walking their floor feels less like a lab and more like a workshop where hardware, software, data operations, and customer support are tightly integrated around a single goal of building a generally capable, software-defined industrial workforce.

We have long believed in the power of compounding data loops, particularly when the data is difficult to obtain. In software, unique data has grown harder to defend, but in robotics, the opposite is true. Every robot Tutor deploys becomes another node in a network collecting valuable, hard-to-replicate data that continually strengthens the system. And by pricing robots by the hour of runtime instead of as capital expenditure, Tutor makes automation accessible to mid-sized manufacturers and family businesses that have never been able to automate before. Its as effective and affordable for an auto manufacturer as it is for the long tail of mom and pop small businesses.