AI Robots Learn Simple Human Tasks at Massachusetts Factory in Robotics “Kindergarten”
Watertown, MA — In Watertown, Massachusetts, a new data factory trains AI robots. The site builds strong links between tasks and data. It is the largest AI robot data factory in the United States. The team calls it a robotics “kindergarten.” Two MIT graduates, Josh Gruenstein and Alon Kosowsky-Sachs, started this project. They came from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and formed Tutor Intelligence.
Artificial intelligence grows fast in language and image work. But robots learn human tasks slowly because few examples exist. AI models like ChatGPT read huge amounts of text. In contrast, robots need physical task data. That data must be made from scratch.
“Most people live their lives and never see a robot. That’s going to change really quickly,” Gruenstein said as Tutor’s CEO. “These robots do basic work. They pick up items, put them in boxes, and even try to fold laundry.”
The robots in Watertown are in early stages. They often make mistakes. This hands-on training keeps data close. Robots learn physical tasks in many settings, much like people learn by doing. Today, they perform simple, repeated motions—similar to robots in car factories.
“It is easy to build a robot that repeats one job very well,” Gruenstein noted. “But most work does not follow one strict pattern.”
The goal now is to raise robots that learn new skills on the fly. Over 100 robots work inside the factory. They build training data by practicing tasks every day.
“At the pace of this technology, change is fast,” Gruenstein added. “Soon, you will go about your day and see a robot. I expect a fascinating shift in technology and society in the next five years.”
This work marks a major step in robotics and AI. It points to a future where robots help with daily tasks beyond factory lines—shaping industries and everyday life.
For more on advancements in robotics and AI, stay tuned to CBS Boston.
Reported by Alyssa Andrews, CBS Boston
April 24, 2026 | 6:02 PM EDT
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