Robert Playter

I've seen it land in different configurations and it still manages to stabilize itself.

And so, you know, what this model predictive control means is, again, in real time, the robot is projecting ahead, you know, a second into the future and sort of exploring options.

And if I move my arm a little bit more this way, how is that gonna affect the outcome?

And so it can do these calculations, many of them,

and basically solve where, given where I am now, maybe I took off a little bit screwy from how I had planned, I can adjust.

Adjust on the fly.

So the model predictive control lets you adjust on the fly.

And of course, I think this is what people adapt as well.

When we do it, even a gymnastics trick, we try to set it up so it's as close to the same every time, but we figured out how to do some adjustment on the fly.

And now we're starting to figure out that the robots can do this adjustment on the fly as well, using these techniques.

Well, that's sort of the, you talked about underactuated, right?

So when you're in the air, there's some things you can't change, right?

You can't change the momentum while it's in the air because you can't apply an external force or torque.

And so the momentum isn't going to change.

So how do you work within the constraint of that fixed momentum to still get from A to B where you want to be?

Can't hover.

You're going to impact soon.

Be ready.

that's wild but like uh well we definitely broke a few robots trying but that but that's where the build it break it fix it you know uh strategy comes in you gotta be willing to break and what ends up happening is you end up by breaking the robot repeatedly you find the weak points and then you end up redesigning it so it doesn't break so easily next time you know

Well, I think the courage to do a backflip in the first place and to not worry too much about the ridicule of somebody saying, why the heck are you doing backflips with robots?