While you're not wrong, I suspect that people focusing on this will lead to less fast robotics development.

It's in a similar vein to how "you can prove that a single layer neural network exists that does the same thing as a combination of many neural networks", something that led to a lot of people focusing on single level NNs for "purity's sake" and causing an AI winter.

Like yeah, maybe you don't "need" sensor and motor data. Especially if you build what you're calling a "good" model.

But making a "good" model that gets results might be near impossible and building a "less good" model that does use sensor data, and performs far better on real tasks, might be way easier for us mere mortals to do.

One of the viewpoints covered in the article: "Another set of people argued that we can leverage existing vision, language, and video data and then just ‘sprinkle in’ some robotics data."

Me too. I’m feeling that things have not changed that much. Many companies have been started since 2023 to see if they can get the giant neural net approach to work and we have seen incremental progress based on demo videos. Meanwhile Tesla’s self driving tech is still struggling. But at the same time, robots can now do things that were impossible using classical approaches.

You'd expect someone who's never seen a kitchen before to be able to cook?

A persistent strain of thought assumes static robotics models are useless unless they're trained on the exact environment they operate in, and I'm not sure why.

That aside, arguments from handwaving about biology are extremely weak - this is the same proof structure used to explain that because we only have two eyes, a machine only needs 2 cameras to drive a car safely. It wants you to forget everything that happens after the light hits the retina, and difference between an always-wet lens that's cleared every second or two and a camera. i.e. it's a noble goal to have some model that never saw a kitchen also be an expert chef, but, there's no logical reason to claim that'd ever be the case.