It really depends on your application. If your system is so complex that you give up on understanding the dynamics, then a "generic" ML algorithm makes sense. However, a big problem with applying ML to robotics is you need a ton of data and producing a representative dataset for your system can be hard. Traditional control methods don't need nearly as much data, including the system discovery process.
I think that depends on how good our physical simulations become? If we can model our robot to a sufficient degree of physical precision, and our world simulation is good enough, with the right objective functions it seems like one probably build a robust control system without really understanding any of the control theory at all? Granted this might take the equivalent of 50 years worth of learning but it seems possible? This is how animals have learned to move over time right?
Why is it a useful goal to not understand control theory?
Animals like us have plenty of control theory built into our firmware, even if we aren't going through the calculations on the cognitive software level.
A control system, tuned and running, is a few lines of code in a fast loop, or a feedback amplifier circuit. Just like a neural net, it takes a while to tune the coefficients but then it becomes muscle memory. And since much of control theory is fully general (like Bode's sensitivity integral), those principles must also hold for biological systems as well.
A baby doesn't think about Laplace transforms when learning to walk, but neither do they think about backpropagation.
If you're keen to replace control theory with something else, I recommend solidly understanding it first.
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.
