A former google AI researcher speculated on my facebook feed that google might want to use their Deepmind tech across all their AI ventures, and that Boston Dynamics, having taken a very different path, might not be in an easy position to switch over.
I always assumed practical non-trundling robotics would have a "Don't fall over" layer, a "walk this way" layer, and a "Let's see what's over there" layer - much like the layers in a human brain.
Deepmind would be good for strategy and goal setting, but maybe not so great at the millisecond-to-millisecond control needed to not fall over - which is the layer BD seem to have solved.
That's about right. Getting through the next few seconds in life is mostly about not falling over and not bumping into stuff. Once you have that, you can back-seat drive it with goal oriented systems.
On the other hand, using machine learning to tune your lower level control loops is extremely useful. (I used to think this was a path to AI, and went on a long detour through adaptive feedforward control and system identification. But in the end, machine learning did more for control theory than control theory did for machine learning.)
