The former approach works fine for a factory floor robot that is in a controlled environment but doesn't lend itself well to other situations. An example might be a biped robot walking up a mountain and it falls. Most of the examples I've seen, the Boston Dynamics one included, have the robot detect it's falling and put itself into a crash position where it remains until it comes to a rest before attempting to recover.
If your robot is rolling down a mountain it might be destroyed before it comes to a rest. Having a feedback loop, reflex reactions and the ability to access the situation and recover dynamically would be much more useful.
Agreed. But none of that implies learning. So why are you talking about learning in the beginning of your comment?
This happens all too often in AI conversations. Learning gives you a special and powerful kind of flexibility, of course. But not being able to learn doesn't imply it can't cope with an infinite range of situations. A robot that's unable to learn could be programmed with enough flexibility to walk on any surface it could possibly encounter.
How exactly?
Basically, flexibility and the ability to deal with novel situations is close to synonymous with the ability to learn.