The gig workers who are training humanoid robots at home

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Narrated by NOAA. Listen to more of the best articles from the world's biggest publishers on the NOAA app. Or at newsoveraudio.com. Michelle Kim writes: The gig workers who are training humanoid robots at home. When Zeus, a medical student living in a hilltop city in central Nigeria, returns to his studio apartment from a long day at the hospital, he turns on his ring light, straps his iPhone to his forehead, and starts recording himself. He raises his hands in front of him like a sleepwalker and puts a sheet on his bed. He moves slowly and carefully to make sure his hands stay within the camera frame. Zeus is a data recorder for MicroOne, a US company based in Palo Alto, California, that collects real-world data to sell to robotics companies. As companies like Tesla, Figure A1, and Agility Robotics race to build humanoids, Robots designed to resemble and move like humans in factories and homes, videos recorded by gig workers like Zeus are becoming the hottest new way to train them. MicroOne has hired thousands of contract workers in more than 50 countries, including India, Nigeria, and Argentina, where swathes of tech-savvy young people are looking for jobs. They're mounting iPhones on their heads and recording themselves folding laundry, washing dishes, and cooking. The job pays well by local standards and is boosting local economies, but it raises thorny questions around privacy and informed consent, and the work can be challenging at times and weird. Zeus found the job in November 2025 when people started talking about it everywhere on LinkedIn and YouTube. This would be a real nice opportunity to set a mark and give data that will be used to train robots in the future, he thought. Zeus is paid $15 an hour, which is good income in Nigeria's strained economy with high unemployment rates. But as a bright-eyed student dreaming of becoming a doctor, he finds ironing his clothes for hours every day boring. "I really do not like it so much," he says. "I'm the kind of person that requires a technical job that requires me to think." Zeus and all the workers interviewed by MIT Technology Review asked to be referred to only by pseudonyms because they were not authorized to talk about their work. Humanoid robots are notoriously hard to build because manipulating physical objects is a difficult skill to master. But the rise of large language models underlying chatbots like ChatGPT has inspired a paradigm shift in robotics. Just as large language models learn to generate words by being trained on vast troves of text scraped from the internet, many researchers believe that humanoid robots can learn to interact with the world by being trained on massive amounts of movement data. Robotics requires far more complex data about the physical world though, and that is much harder to find. Virtual simulations can train robots to perform acrobatics, but not how to grasp and move objects, because simulations struggle to model physics with perfect accuracy. For robots to work in factories and serve as housekeepers, real-world data, however time-consuming and expensive to collect, may be what we need. Investors are pouring money feverishly into solving this challenge, spending over $6 billion on humanoid robots in 2025. And at-home data recording is becoming a booming gig economy around the world. Data companies like Scale AI and Oncord are recruiting their own armies of data recorders. While DoorDash pays delivery drivers to film themselves doing chores. And in China, workers in dozens of state-owned robot training centers wear virtual reality headsets and exoskeletons to teach humanoid robots how to open a microwave and wipe down the table. "There is a lot of demand, and it's increasing really fast," says Ali Ansari, CEO of MicroOne. He estimates that robotics companies are now spending more than $100 million each year to buy real-world data from his company and others like it. Workers at MicroOne are vetted by an AI agent named Zara that conducts interviews and reviews samples of chore videos. Every week, they submit videos of themselves doing chores around their homes, following a list of instructions about things like keeping their hands visible and moving at natural speed. The videos are reviewed by both AI and a human, are either accepted or rejected. They're then annotated by AI and a team of hundreds of humans who label the actions in the footage. Because this approach to training robots is in its infancy, it's not clear yet what makes good training data. Still, you need to give lots and lots of variations for the robot to generalize well for basic navigation and manipulation of the world, says Ansari. But many workers say that creating a variety of "chore content" in their tiny homes is a challenge. Zeus, a scrappy student living in a humble studio, struggles to record anything beyond ironing his clothes every day. Arjun, a tutor in Delhi, India, takes an hour to make a 15-minute video because he spends so much time brainstorming new chores. "How much content can be made in the home? How much content?" he says. There's also the sticky question of privacy. MicroOne asks workers not to show their faces to the camera or reveal personal information such as names, phone numbers, and birthdates. Then it uses AI and human reviewers to remove anything that slips through. But even without faces, the videos capture an intimate slice of workers' lives—the interiors of their homes, their possessions, their routines. And understanding what kind of personal information they might be recording while they're busy doing chores on camera can be tricky. Reviews of such footage might not filter out sensitive information beyond the most obvious identifiers. For workers with families, keeping private life off camera is a constant negotiation. Arjun, a father of two daughters, has to wrangle his chaotic two-year-old out of frame, "Sometimes it's very difficult to work because my daughter is small," he says. Sasha, a banker turned data recorder in Nigeria, tiptoes around when she hangs her laundry outside in a shared residential compound so she won't record her neighbors, who watch her in bewilderment. While the workers interviewed by MIT Technology Review understand that their data is being used to train robots, None of them know how exactly their data will be used, stored, and shared with third parties, including the robotics companies that MicroOne is selling the data to. For confidentiality reasons, says Ansari, MicroOne doesn't name its clients or disclose to workers the specific nature of the projects they are contributing to. It is important that if workers are engaging in this, that they are informed by the companies themselves of the intention "Where this kind of technology might go and how that might affect them longer term," says Yasmin Kothuri, a professor of human-centered computing at the University of Maryland. Occasionally, some workers say they've seen other workers asking on the company Slack channel if the company could delete their data. MicroOne declined to comment on whether such data is deleted. People are opting into this, says Ansari. They could stop the work at any time. With thousands of workers doing their chores differently in different homes, some roboticists wonder if the data collected from them is reliable enough to train robots safely. "How we conduct our lives in our homes is not always right from a safety point of view," says Aaron Prather, a roboticist at ASTM International. "If those folks are teaching those bad habits that could lead to an incident, then that's not good data. And the sheer volume of data being collected makes reviewing it for quality control challenging. But Ansari says the company rejects videos showing unsafe ways of performing a task, while clumsy movements can be useful to teach robots what not to do. Then there's the question of how much of this data we need. Microwon says it has tens of thousands of hours of footage, while Scale AI announced it had gathered more than 100,000 hours. It's going to take a long time to get there, says Ken Goldberg, a roboticist at the University of California, Berkeley. Large language models were trained on text and images that would take a human 100,000 years to read, and humanoid robots may need even more data because controlling robotic joints is even more complicated than generating text. "It's going to take longer than people think," he says. When Dattu, an engineering student living in a bustling tech hub in India, comes home after a full day of classes at his university, he skips dinner and dashes to his tiny balcony, cramped with potted plants and dumbbells. He straps his iPhone to his forehead and records himself folding the same set of clothes over and over again. His family stares at him quizzically. "It's like some space technology for them," he says. When he tells his friends about his job, they just get astounded by the idea that they can get paid by recording chores. Juggling his university studies with data recording, as well as other data annotation gigs, takes a toll on him. "Still, it feels like you're doing something different than the whole world," he says. You were listening to MIT Technology Review. Where Michelle Kim writes, "The Gig Workers Who Are Training Humanoid Robots at Home." This article was published on April 1st, 2026, and was read by Martin Buchanan for NOAH.