It feels like this issue have been (at least in academia) known for a long time. When I try to explain the problem I usually use the slides from ICML 2018 talk by Yisong Yue[1]. And there's already (hard to use in practice) methods such as DAgger to correct for that accumulating error.
Out of the more recent advancements, I think ACT[2]s approach seems like the more interesting direction. Similar to MPC that predict(calculate) not only the next action but predict sequences of actions, discard all the actions except for one, at each loop.
Mapping the input to only the current action seems to be prone to error, while mapping the multiple steps is more robust.
My hunch is that there's probably some pretty fundamental limitations to our current methods (Transformers etc) though, which makes applying it to things such as language when error accumulation is essentially neglible (relatively speaking) fine but, to cars and robots maybe not.