Development of self balancing biped with inverse Dynamics

[Julio Jerez]

ok, guys this is the moment we have been waiting for.


I dare anyone to pick an object from the scene.

known issues, if you collide with the object, the arm can push them through the ground very easy.
the problem can be fixed, but for now let us leave it like that.

the joints are migthy powerful, but there many ways to address that. in reality, if a real robot does that, it will most likely destroy the box, damage itself, or triger some safety to will make it stops. we can mimic that behavior.
They easiest way to deal with that is to lower the arm torques limits, right now it is almost unlimited.

anyway, I think with this, other than some tweaks here and there, this prove of concept is a done deal.

Now we can move to the next application of this solver.

I was thinking that before moving to a biped, maybe is better to try a quadruped like the Robot spot from Boston Dynamics. A biped combines two problem at once.
- how to cordinates multiple effectors
-the self banking.

While a quadruped removes the balance of the problem. Them after we master the coordination of multiple effectors, we move on with the biped.

this will be my version of spot.

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[JoeJ]

Haha, lifted the box without issues. All precise and perfect.
Code super easy as well.

Btw, i posted the ML video just to show state of competition. But if you're eager to become ML expert, just move ahead. There are similar works with ML driving physics simulation, like this: https://www.youtube.com/watch?v=eksOgX3vacs Though, that's the stuff i could do traditionally and without any mocap samples, i'm sure of.

Another impressive one, aiming to reduce input latency: https://www.youtube.com/watch?v=14tNq-fqTmQ

[Julio Jerez]

if you look at this video,
https://www.youtube.com/watch?v=zJz2HtPUolk
https://www.youtube.com/watch?v=KlFO8QMXVCs
it is alone the line of what I wish to achieve, but dynamics and parametric.

if we get a self balancing that operate after a procedural footstep generator, them it is just a matter of solving the footstep placement in the environment the physics does the rest.

[JoeJ]

Yeah, that's also my goal.
Above 3Ds videos do not look very natural, but once you add balancing, it becomes natural automatically without mocap.
And we have precise control and better performance.

Main advantage of ML seems they can add many skills with very little work, if they have proper samples.
Opposed, it likely would take me weeks or months to get some melee boxing or karate to work, even if i already had walking and running.
And because i know nothing about martial arts (did not even play Street Fighter), my results may end up pretty bad.
But i'm more interested in shooting anyway, which is simple.

Though, looking into my crystal ball, i can well imagine we are close to a ML revolution affecting games, and character simulation seems the proper application. A company like Epic may add this to their Meta Humans to offload the training hurdle from users. And boom - suddenly indies can use characters like the big boys. This could have some good effect on games, but also a bad one by further declining custom engines, which blocks innovation on the long run.

I'll continue to observe the AAA Titanic from a distance to find out...

[Julio Jerez]

Though, looking into my crystal ball, i can well imagine we are close to a ML revolution affecting games, and character simulation seems the proper application. A company like Epic may add this to their Meta Humans to offload the training hurdle from users.

I do not think that will happen any time soon.
Neural networks are regression calculator. all they do is that the fix the coefficient of a curve is multiple dimensions. before people were doing that with principal component analysis, and Gaussian networks. and even them we very difficult. everything you can do with a neural network, can also be done with a Gaussian Network at a smaller scale.

basically, what a network does is that takes an entire animation clip as a single sample in a search space. and them from those samples can gives you the set of all sample that are close to the space.

it is not that simple to program neural network. we are living is a world where misinformation is what sales. all the fuss you see about neutral networks is the bit product of one algorithm called backwoods propagation, that allows for networks with hundreds of layers to be programed efficiently

and since Google and apply bought them, every one jump into the band wagon, even IBM though the were going to be able to find a cure for cancer with neural networks and instead end up playing Jeopardy.
The investment in programming neural network is just too high for video games. when the same thing can be done using blending graph.

[JoeJ]

basically, what a network does is that takes an entire animation clip as a single sample in a search space. and them from those samples can gives you the set of all sample that are close to the space.

That's pretty much how i imagined it to work. I won't go there - feel too old to learn it. I would be happy if could learn basics of math as you explained some posts above in my lifetime ;D

However, despite being always critical with hyped stuff, i became more open minded towards ML.
It first occurred to me here on this forum. Pretty early when i came here, and i think this was before the hype. A ML researcher contacted me by PM because he was interested in using it for character animation. We talked a bit, and he told me things hard to believe. He said his stuff could even learn to talk. Though, might take ten years for the training.
Now, all he said became true. A year ago i had a intellectual conversation with a ML chatbot. It did not pass the turing test on me, but the talk was meaningful. The bots reactions to my cynical remarks about the meaning of life from the perspective of an AI bot were eerie relaxed and almost outsmarting, reflecting my trolling with simple truth.

But what really convinced me was this: https://soundcloud.com/yichao-zhou-555747812/sets/bandnet-sound-samples-1
To me, as i'm a hobby musician, i always said 'ML will never be able to compose earwigs'. That's the highest art in music. Nobody has a recipe. We simply don't know why some melodies stick in our heads. And if you compose one yourself, it feels like a gift coming from the outside. You just pick it up and eventually refine it.
Those examples from the link are generated by ML from Beatles samples. And the songs are, while somewhat unconventional, pretty good. I know many human composer who never get there in their whole life. The examples surely are cherry picked, but that's no argument. Finding one good song from a million examples would be not much different than composing one yourself. Thus, many songs the ML has generated must have some higher quality i guess.
So, ML achieved what i personally thought would be impossible for a machine.
The magic surely comes from the Beatles samples, which are human, but the resulting songs are new ones. And the first one is really catchy. Could become a radio hit or at least a good commercial.

ML really seems useful. Especially for tasks which are fuzzy and lack a precise definition of the problem. Which i think is not true for our robotic topic here.
Still, my crystal ball remains showing me the picture of games industry jumping on this. You explained the reasons yourself.