Covariant

“An average warehouse that we serve have typically more than a hundred percent year over year turnover rate.”

“You kind of don't need that when you are in a more Restricted domain, like robotics because like you actually could have so much data coverage that your test scenarios are just part of your training scenario. So to some degree, like we actually don't need to r…”

“And the only way to collect enough data is to build fleets of robots that are actually creating value for customers that, so that you can collect those data in production. Because even if you try to scale up data collection in a lab environment, There's a limi…”

“The most technical definition of scaling law does apply and we have seen it apply in this domain. And it's somewhat not surprising because like if you think about like the scaling law in the most technical sense, which is if you scale up data and you scale up …”

“I would say identification and routings are typically more considered a more solved problem than grasping. Like, because if you, there are other, like, more mechanical way to solve those problems. Like, you can design a piece of conveyor that, like, if you alw…”

“And so we found robotics to be such a great way to both utilize the advances in AI, but also we think of it as a way to also propel AI forward. Like this is where you get the grounded data. This is where you get that embodied data of not just AI that is traine…”

“All of these customers, all of these different robots are networked together. Like it's one single foundation model. And everything that they learn come back and make this central model better.”