most robots are energetically inefficient and require extraneous computational power because they don't take advantage of passive dynamics (https://en.wikipedia.org/wiki/Passive_dynamics). it's disappointing to realize that we still haven't caught up to fundamental biological systems yet, even though the research is 2+ decades old.

"Preflexes are the latent capacities in the musculoskeletal system that auto-stabilize movements through the use of the nonlinear visco-elastic properties of muscles when they contract.[1][2] The term "preflex" for such a zero-delay, intrinsic feedback loop was coined by Loeb. [3] Unlike stabilization methods using neurons such as reflexes and higher brain control, it happens with minimal time delay. Its chief disadvantage is that it works only to stabilize the main movements of the musculoskeletal system."

somewhat related to that, our tendons are tuned to our gait dynamics to return the optimal energy back to us as we locomote (more accurately, our strides optimize around tendon elasticity and leg length).

Given that we're the result of billions of years of natural selection, shouldn't we expect that?

organisms have evolved over billions of years to take advantage of passive dynamics, which is basically the application of newtonian physics to what is mostly a mechanical design problem. we've known for a long time that passive dynamic elements are not only energetically more efficient, but also makes computations simpler.

by contrast, active dynamic locomotion is akin to putting a joint between the front fork of a bike and the shaft, and installing a hand pump that you have to constantly actuate to keep the bike stable while you're riding it. you could do it, but it's not the best solution.

By the same token, most of the tooling and parts for building airplanes isn't all that useful for building orinthopters.

So it's not just knowledge. It's knowledge expressed in the form of industrial infrastructure and products.

This isn’t a kinematics homework where you can draw the links and joints and get full marks. You have to actuate them. For a human gait, you need to generate the same torque, at the same velocities, and the mass of the actuator needs to distribute across the linkages in the same proportion. Guess what? Actuators like that are hard to find.

I should paraphrase that for interviews. "This isn't a CS homework where you can cite Big-O and get full marks. We have to be able to implement code that uses them."

And by that, I don't mean that you have to write all of the actual code. But one needs to be able to specify it exactly so someone could implement it and know enough about that to tell someone of the pitfalls in the current problem context/framework.

If you showed me a chat bot and a walking robot, I would say that the walking robot is orders of magnitudes closer to being human, even though it's just a glorified Segway.

Very funny how that works.

I figure that’s probably what I’m imagining when I see theee videos.

If I ever lock my joints I feel like I'm going to fall over too. I wonder if a more human walk might just be less efficient for bipedal movement and balance.

Also, I'm betting that by March we will see Atlas do a Fortnite dance.

That's a dynamic situation though, locking joints in a static situation pretty much requires thinking about balance, so you'd notice perturbations more.

Real gaits though really need a foot with four degrees of freedom rather than the more common three. Typically the 'foot' plate has a roll, pitch, and yaw component. If you add an additional bend access where humans and animals have the 'ball' of their foot so that you have a flat surface on the ground while "up on your toes" to can add some additional gait styles.

[0]https://medium.com/@froger_mcs/moravecs-paradox-c79bf638103f

It is certainly quite impressive. But it seems to me that the paradox is neatly solved by comparing the how well-constrained the activity is.

Movement and (especially) visual processing are incredibly general problems. Where as board games are very narrow, well-constrained problems.

It always interesting when people expect AI to robustly and effectively process visual content without first actually understanding the world (which obviously no AI is even close to doing).

Somehow I have the feeling that robots capable of striding confidently over mountains of human skulls could be coming sooner than some think...