Dear Fellow Scholars, this is Two Minute Papers with Dr. Károly Zsolnai-Fehér.
Today, it is possible to teach virtual characters to perform highly dynamic motions, like a
cartwheel or backflips.
And not only that, but we can teach an AI to perform this differently from other characters,
to do it with style if you will.
But!
Today, we are not looking to be stylish.
Today, we are looking to be efficient!
In this paper, researchers placed an AI in a physics simulation and asked it to control
a virtual character and gave it one task: to jump as high as it can.
And when I heard this idea, I was elated, and immediately wondered - did it come up
with popular techniques that exist in the real world?
Well, let’s see…yes!
Woo-hoo!
That is indeed a Fosbury flop.
This allows the athlete to jump backward over the bar, thus lowering their center of gravity.
Even today, this is the prevalent technique in high jump competitions.
With this technique, the takeoff takes place relatively late.
The only problem is that the AI didn’t clear the bar so far…so, can it?
Well, this is a learning-based algorithm, so, with a little more practice, it should
improve - yes, great work!
If we lower the bar just a tiny bit for this virtual athlete, we can also observe it performing
the western roll.
With this technique, we take off a little earlier and we don’t jump backward, but
sideways.
If it had nothing else, this would already be a great paper, but we are not nearly done
yet.
The best is yet to come!
This is a simulation.
A virtual world if you will, and here, we make all the rules.
The limit is only our imagination.
The authors know that very well and you will see that they indeed have a very vivid imagination.
For instance, we can also simulate a jump with a weak take-off leg and see that with
this condition, the little AI can only clear a bar that is approximately one foot lower
than its previous record.
What about another virtual athlete with an inflexible spine?
It can jump approximately two feet lower.
Here is the difference compared to the original.
I am enjoying this a great deal, and it’s only getting better.
Next, what happens if we are injured and have a cast on the take-off knee?
What results can we expect?
Something like this.
We can jump a little more than two feet lower.
What about organizing the olympics on Mars?
What would that look like?
What would the world record be with the weaker gravity there?
Well, hold on to your papers, and look… yes, we could jump three feet higher than
on earth, and then…ouch, well missed the foam matting, but otherwise, very impressive.
And if we are already there, why limit the simulation to high jumps?
Why not try something else?
Again, in a simulation, we can do anything!
Previously, the task was to jump over the bar, but we can also recreate the simulation
to include instead, jumping through obstacles.
To get all of these magical results, the authors propose a step they call “Bayesian Diversity
Search”.
This helps systematically creating a rich selection of novel strategies, and it does
this efficiently.
The authors also went the extra mile and included a comparison to motion capture footage performed
by a real athlete.
But note that the AI’s version uses a similar technique and is able to clear a significantly
higher bar without ever seeing a high jump move.
The method was trained on motion capture footage to get used to humanlike movements, like walking,
running, and kicks, but it has never seen any high jump techniques before.
Wow.
So, if this can invent high jumping techniques that took decades for humans to invent, I
wonder what else it could invent?
What do you think?
Let me know in the comments below!
What a time to be alive!
Thanks for watching and for your generous support,
and I'll see you next time!