[1arity]

I think the biggest thing Boston Dynamics are dealing with ( aside from power density ) are improving balance and running algorithms, under the stresses of heavy loads, non-homogenous terrain, and loss of friction.

It's possible to use deep learning for controlling the servo motor positions and torques, the hierarchy of features is then varying levels of abstraction over the set of positions. The most granular layer may be features related to all the torques and positions of the servo, a higher layer may be some combinations of subsets of these, and the highest level may be features akin to the general representation of the spatial position and tension of the limb.

It's quite naturally similar to how human kinetic intelligence and perception works, say you are learning how to dance, you can be aware of the position of all of your limbs simultaneously ( the general pose you are adopting ), or you can be aware of the angle and position of your ankle or your right hip independently ( what your foot is doing ).

I think there could be things to learn from the networks in octopus limbs -- because of their extensive innervation.