The push to bring humanoid robots into homes has created a surprising new type of job: recording everyday chores. All that’s needed is a head-mounted camera, a smartphone, and a list of tasks.
With AI rapidly advancing, humanoid robots are now able to walk, dance, and manipulate objects with increasing precision. Yet the ultimate goal—a versatile robot capable of working in homes, offices, and shops—requires enormous amounts of data to learn how to safely and efficiently perform human tasks. Increasingly, this data comes from people filming themselves doing routine chores.
First-Person Videos: The Key to Training Robots
Startups such as Micro1 have launched programs that hire thousands of contractors worldwide to capture videos of everyday tasks, from cooking and cleaning to gardening and pet care. Workers receive instructions, headgear with cameras, and weekly quotas of at least 10 hours of footage.
“These movements differ in every environment—whether a factory, hospital, or retail space—so robots need this data from multiple settings,” said Arian Sadeghi, VP of robotics data at Micro1. Contractors are encouraged to record creative variations of tasks, helping robots learn to adapt to new conditions.
The Scale of Data Required
Micro1 collects over 160,000 hours of video monthly from 4,000 contractors across 71 countries—but Sadeghi says billions of hours are needed. Similar to how ChatGPT learned from hundreds of billions of words online, humanoid robots require vast amounts of first-person video data to master complex physical tasks.
The videos are annotated to teach robots object recognition, spatial awareness, and human motion patterns. Analysts project the global market for robotics data collection and labeling could reach $10 billion by 2030, expanding roughly 30% annually, led by growth in Asia.
Regional Differences Matter
Ravi Rajalingam, founder of Objectways, emphasizes geographic diversity. “A kitchen in India is very different from one in the US. Tools and layouts matter if robots are deployed there,” he said. Data from US households often commands higher pay, reflecting the assumption that American consumers will be early adopters of humanoid robots.
Blending Real-World and Simulation Training
Previously, robots were trained via remote controls or virtual simulations, both costly or limited for handling physical objects. First-person video from humans provides a practical alternative, allowing robots to learn real-world interactions while keeping expenses lower.
China has invested heavily, opening more than 60 robot training centers. Companies like Tesla are developing their Optimus humanoid in California, while US and European developers often combine simulations with real-world video to improve performance. Nvidia reported that adding 20,000 hours of first-person footage increased task success rates—like folding T-shirts, sorting cards, or unscrewing caps—by more than 50%.
Challenges in Household Automation
Despite progress, robots still struggle in unpredictable home environments, where furniture, appliances, and people constantly change positions. Human intuition about force, friction, and object handling remains difficult to replicate. Even folding laundry, a seemingly simple task, has success rates too low for commercial deployment.
Safety is another concern. “If a robot mistakes a baby for a toy, the consequences could be severe,” said Rajalingam. While testing with infants is still far off, some trials are underway with pets to improve recognition and safe handling.
Recording chores today could directly influence how humanoid robots perform tomorrow, bridging the final gap toward fully autonomous home assistants.
0 Comments