Brian Gerkey

It's like a special purpose computer.

You can do really capable things with it in a relatively narrow context.

But what's difficult is that that software, because it was kind of developed in a time

When you were the applications you were building were of that kind of high volume, highly repeatable sort of blind applications, not strictly, but sort of like you're kind of closing your eyes and then you know where the object is and you're just going to pick it up and you're going to put it down and you're going to optimize the time to do that.

Right.

Right.

there are good tools for that.

What has been more difficult for that software ecosystem, because it is software, right?

But what's been more difficult is how do I bring in the latest sensors?

How do I bring in the latest AI models?

Is there a place in there where I can host the inference that we talked about earlier?

That's certainly part of it.

Another part of it is that

historically the companies making the software have also been the companies making the robots.

And the two have tended to be kind of locked together.

And that's, you know, that's worked well for commercial reasons for some of the players.

Very often the customers, the end customers who are getting the robots would prefer to have a little more flexibility.

And so this is, you know, this is one of the things that, you know, if you go back to the Android analogy, an Android application, a well-formed Android application can run on

kind of any Android device.

And it doesn't matter whether the manufacturer was a, you know, Pixel or Samsung.