Brett Adcock

And the biggest learning lesson we got was that the robots, the robot that started the first day at the start of six months,

And the robot that ended the shift that day was the same, like, even though we had multiple robots in operation every day, we had, like, the same robot that did the start and the finish.

Wow.

And it was cool.

Like, you know, like, it was the same robot ended six months later.

And this was the thing where, you know, the worry was that humanoid robots couldn't last a month, couldn't last a week in these kind of environments.

And, you know, wear and tear, it's like it's running like a, you know, 40 degrees of freedom motors every single day.

Can they operate really well?

I think from a hardware perspective, it did an A-plus job.

I think from a software perspective, we did, I would say, a B job, B-plus.

And that's mostly from my perspective, the architecture decisions I made to scale.

About half the stack, we had traditional code and heuristics in.

So the controller to walk was done by a C++ controller.

It was done by code.

The walk-in you saw today, we had back then, was done in code.

The rest of this, we had a bunch of other stuff in there done by neural nets.

And some of the perception stacks, some of how to move parts around, everything else.

And this was a year and a half or so ago when we were first launching.

And I was like, man, the biggest problems we're having is the coding parts get stuck.

The robot like either doesn't see like the right, like doesn't like see something right on the part and misses the object detector, doesn't really understand what's going on.