[Animats]

Modern control theory is rather close to machine learning. Adaptive model-based feedforward control is machine learning. The machine learning part builds a model of the dynamics of the system. Then that model is inverted (solved for control inputs) to make it a control system.

They're doing this right. They have a very good basic body control system. Now someone can build higher level strategies to get work done on top of that. That's how biological brains work, after all. Google/Alphabet could, for example, reuse much of their automatic driving software as high level control for this robot.

Google should have BD manufacture a few hundred of those machines, and try to get the cost down to $25K or less per unit for that production run.

[argonaut]

Sure, the brain uses the concept of abstraction, but that is so far away from supporting the assertion that "that's how the brain works."

[Animats]

Mammal brains have multiple functional units. The cerebellum does most of the motor control. The cortex does most of the planning and deciding. The cortex acts through the cerebellum, not by driving muscles directly. Most of Boston Dynamics' control systems are doing cerebellum-level functions. As with the cerebellum, this involves fast control via feedback loops.

[argonaut]

Except for the inconvenient fact that the cerebellum is not explicitly solving control theory equations.

And also that other inconvenient fact that neuroscientists barely understand the brain at all.

[Animats]

"Except for the inconvenient fact that the cerebellum is not explicitly solving control theory equations."

It might be. You can invert a model by training a neural net to compute its inverse.

[argonaut]

It might be. It might not be. Hardly a compelling argument.

[sharemywin]

reinforcement learning goes along way too.