Yes the problem is very hard. Mainly because high DOF generalization is very difficult.

We have self driving cars because what are the control inputs? Pedal, brake, steering wheel. This already took many many years.

Now for a humanoid robot: An action space that is metaphorically Hilbert. (Physically, yes, obviously)

Also, IMO, LLM's can aid the development of robots, but do little beyond a planning, human control interface. Below that it's the domain of control and the solution will be the correct combination of classical, neural, and real time optimization based control.

All the bad-ass biped robots that actually look natural? It's PID controls wrapped with control barrier functions constraining the QPs that are being solved in real time.

But that's annoying to derive per-application. So we'll need neural methods which can be learned (while being constrained by a priori knowledge of dynamics). My hunch is that the Yann LeCunn type of jepa models will be how tasks can be learned.

> All the bad-ass biped robots that actually look natural? It's PID controls wrapped with control barrier functions constraining the QPs that are being solved in real time.

That's not entirely true. Locomotion is well addressed by RL in sim. It's true that there is still a PD layer, and the RL policy produces setpoints for it.

> So we'll need neural methods which can be learned

Data is a problem. LLMs had the advantage of the whole internet to train on. Robots don’t have that corpus of information. And real time learning seems to be something that everyone in AI is studiously ignoring.

I don’t think the complexity scales with every additional degree of freedom like you are painting here. I think it’s just a matter of getting the right training data in sufficient quantities for an LLM to output across all degrees of freedom simultaneously without it being some exponential leap.