But they're mostly silent on the matter of how much autonomy the robots are operating with. I never know how much of their behavior is human directed, or how those directions are conveyed to the robots.
I am a little bit disappointed that they are not using deep learning for robotics. Instead, they are simulating the robot based off a model, using Control Theory. It might be easier to get results from Control Theory but it doesn't offer a path towards more complex behavior, like Deep Learning.
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.
And also that other inconvenient fact that neuroscientists barely understand the brain at all.
It might be. You can invert a model by training a neural net to compute its inverse.
I would have to practice with the controls for a really long time to be able to pick up a soda can that deftly, though.
While they are not using deep learning, I think they're doing good work building a platform that can later be controlled via neural net or whatever. The control-theory-based balance and locomotion means that the lowest-level aspects of operation will not demand resources from the neural net and provides the prototypers in the meantime with a way of experimenting to see what body designs might be most effective.
Google is working on the deep learning aspect of finding out what to do / action and how to do it.
The control theory remark above is true, however even with deep learning, you can start your estimate of the parameters with the current control state, which reduces training time significantly.