This sounds like a problem of a lack of scale. Intuitively I do not think there can ever be any theoretical solution for this with no assumptions on the real wo

This sounds like a problem of a lack of scale. Intuitively I do not think there can ever be any theoretical solution for this with no assumptions on the real world. Essentially let us say you have a world with data from a distribution X. Now you have a dataset with data from the distribution Y. Your problem boils down to the fact that when you run a model trained on the distribution Y, it regular ends up in states in the distribution {X-Y } for which it does not know what to imitate to, which leads it further into out of distribution land and so on till your car crashes. Now unless you limit the scope of X, you cannot come up with any real world guarantees, but in the real world there is no scope on X. In the real world tomorrow a road could cave, which would likely be so OOD, no model (or human driver) in the world can save you. So really your only solution intuitively is if Y is so large and expansive, it models X for all the cases we care about (reaching human driver performance), or if your model is so big and generalizable that it generalizes to a good fraction of X being trained on Y. With deep learning we have seen generalization due to both more data and larger model with more data showing much greater improvements and that might be the only solution to this dilemma. Also a little bird told me, in Tesla’s latest end to end approach, they train the model, run it in a car with a driver they hired, the driver then intervenes at the last moment, they take the driver data and add it back to the dataset and do this as infinitum with their hope of solving self driving this way. The problem with robotics, is even if you assume with data scale these problems are solved, in robots you are strictly limited in the model sizes you can use. Too big a model will not run in real time and make it effectively useless. But having a smaller model goes against all the trends we’ve seen in deep learning. This really just makes it exponentially harder to solve robotics compared to language or vision with our current paradigm of deep learning, I won’t put it out of the realm of possible though. A few years back I strongly thought language would not be solved this way and I could not be more wrong.