That presumes the existence of an arbitrarily precise simulator, that you can run efficiently enough to generate the large datasets required for deep learning.

That presumes the existence of an arbitrarily precise simulator, that you can run efficiently enough to generate the large datasets required for deep learning. That's a tall ask for not a lot of gain. We've understood rigid-body dynamics for hundreds of years. Finding the equations of motion for a system is just running an algorithm at this point. Why not use what we know? That said, deep learning is great for when we can't model things well. For example, there are a number of mathematical issues with how we model object grasping even before visual data is introduced. Since the setting is so difficult to work with analytically, there have been a number of exciting data-driven solutions proposed in this space.