u/Historical-Iron6343

A large amount of egocentric video doesn't necessarily mean you have a useful dataset for Physical AI. Things like camera placement, whether the hands and objects are clearly visible, task variety, environment, motion quality, and consistency between recordings all seem like they could make a big difference. I'm especially interested in how teams collecting this kind of data in India handle quality before the recording starts. Do you normally have a collection spec or checklist that people follow, or do you figure out what footage is usable after it's been recorded?

For those collecting egocentric data for Physical AI, I’m interested in what becomes difficult once the basic recording setup is in place. Getting consistent first-person footage across different environments seems like one challenge, but there are also things like defining collection protocols, keeping camera placement consistent, handling different lighting conditions, and getting people to perform tasks naturally while being recorded. India has such a wide range of physical work and environments that I imagine making the resulting data consistent and useful for training is a much bigger challenge than simply recording the footage.

For those people working on this, where do you see the biggest bottleneck?

Most of the AI stuff I see here is about models, llms, coding, or gpus. I recently started looking into the data side and realized there’s a whole process around collecting and preparing real-world data before it can actually be useful for AI. We’re building a community in India around learning how to collect better datasets, including pov data, and understanding what makes the data valuable. From what I’ve seen, there are also companies in the US looking for this kind of data, which could make this an interesting skill to learn here. Has anyone in India already looked into this side of AI?