[orliesaurus]

Do I understand this right..:

- You have a simulator, like idk The Sims world

- You train a machine in this world, totally virtual

- You deploy the trained machine on real hardware in the physical world

- The machine works good enough that it can perform the task until it can't anymore and then someone remotes-in to fix it?

Did I get this right or can anyone ELI5 to me? I mean it sounds really cool, in theory...

[iliabara]

Hey!

We don't train in simulation (if you are talking about ML training). Our ML training is done purely using real world data.

The simulator is to allow developers to "test drive" our autonomy core and API for free.

We have real vehicles as well, but you can imagine it's more expensive, time consuming, and generally slower to let people test on real hardware. Hence, if people like what they see in sim, they can get in touch with us to deploy their code to real machines on a case-by-case basis.

[stefan8r]

To add to what Ilia said - re: - "The machine works good enough that it can perform the task until it can't anymore and then someone remotes-in to fix it?"

The machine will work good enough for basic autonomy, but then the real application-specific work begins. Whether that work is you modifying the behaviors you're having us do, you routing those systems to humans to help out, or you complaining that our stuff sucks (and us trying to rapidly improve it).

[hic]

Do you see the platform then providing for now "reptile" autonomy (mobility, navigation, localisation) to get started fast, so customers can focus on their "neo-cortex" applicative needs?

(please let me emphasize this "reptile" autonomy is today perhaps the hardest/time-consuming part killing many startups, as you nicely explain in the OP---Moravec's paradox)

[iliabara]

That's a great way to think about it, you are describing it exactly correctly.