> Doesn't seem to be grounded at least with reality and my personal experience with robotics.
It also doesn't match my personal experience with physics nor ML, and I have degrees in both.
You cannot develop accurate world models through observation alone, full stop.
You cannot verify accurate world models through benchmarks alone, full stop.
These have been pain points in physics for centuries and have been the major pain point even before the quantum revolution. I mean if it were possible, we'd have solved physics long ago. You can find plenty of people going back thousands of years boldly claiming "there is nothing new to be learned in physics," yet it was never true and still isn't true even if we exclude quantum and relativity.
Side note: really the paper is "fine" but I wish we didn't put so much hype in academic writing. Papers should be aimed at other academics and not be advertisements (use the paper to write advertisements like IFLS or Quanta Magazine, but don't degrade the already difficult researcher-to-researcher communication). So I'm saying the experiments are fine and the work represents progress but it is over sold and the conclusions do not necessarily follow
Btw, the paper makes these mistakes too. It makes a very bold assumption that counterfactual models (aka a "world model") are learned. This cannot be demonstrated through benchmarking, it must be proven through interpretability.
Unfortunately, the tail is long and heavy... you don't need black swan events to disrupt these models and boy does this annoying fact make it easy to "hack" these types of models. And frankly, I don't think we want robots operating in the wild (public spaces, as opposed to controlled spaces like a manufacturing floor) if I can make it think an iPhone is an Apple with just a stickynote. Sure, you can solve that precise example but it's not hard to come up with others. It's a cat and mouse game, but remember, Jerry always wins.