Debunked: Are everyday tasks too "ordinary" for training AI?

A lot of people assume that the data behind physical AI, meaning robots and other systems that act in the real world, must come from skilled, dramatic, or highly technical activity. Ordinary tasks like making coffee, wiping a counter, or sorting laundry feel too routine to be useful.

In essence: if it’s a normal day, it’s not valuable data.

This myth likely stems from the complexity of typical text-based AI training these days. As AI models have progressed and been adapted to niche fields like medical imaging, STEM research, and more, training has also become more advanced. But it didn’t start out that way, and that’s where physical AI is right now.

A chatbot can learn from the written internet. A robot can't. It has to learn how hands, objects, and spaces actually interact. It needs to understand how much force it takes to grip a mug, how a drawer slides open, what a cluttered countertop looks like from the point of view of someone standing at it. That's why physical AI data projects typically look like this:

• You apply and get matched to a project: There are no CVs or interviews required. Eligibility depends on your background (location, language, and more) and setup (typically a modern smartphone).

• You follow project guidelines: Guidelines on recording projects are strict and need to be met perfectly — a child visible in the background for a brief second disqualifies a video clip; a noisy room disqualifies an audio recording. 

• You record first-person footage of everyday tasks: These might range from conversations about different topics to videos of you doing laundry or cooking dinner. 

• You contribute until the project's data needs are met: Once the dataset is complete, the project is finished, but there’s often another one around the corner! 

Nothing about the task needs to be impressive. It needs to be real, captured from the right perspective, and varied. Everyday tasks are exactly what robots are being built to help with, so they're exactly what the data needs to cover.

Ego4D, one of the largest first-person video datasets, contains 3,670 hours of daily-life activity across household, outdoor, and leisure settings, among others. It was recorded by 931 people in 74 locations across nine countries, and parts of it include audio, eye gaze, and 3D scans of the environment (Grauman et al., CVPR 2022).

In tests on a set of manipulation tasks, scaling up first-person human hand data outperformed scaling up an equivalent amount of data collected on a robot. And a 2026 preprint (not yet peer-reviewed) found that scene diversity played a dominant role in how well robots generalized to new environments, especially when data was limited.

Researchers building robot-learning systems keep reaching the same conclusion: variety is where the value is. In other words, the unusual tasks don’t train robots (yet), real-world, messy, human experiences do. 

Somewhere down the line, a robot will walk into a home it has never seen before and have to figure out where things go, how things open, and what a person actually needs. Whether it manages depends on how many real homes, hands, and habits it learned from.

A lab can't invent thousands of different kitchens, and a script can't capture how people really move through a day. It takes ordinary people recording ordinary tasks, in places that look nothing alike. So if you've ever wondered whether an ordinary day could be useful to anyone, in this corner of AI, it is.