Running a robot data factory requires custom hardware and logistics

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 12:13

   about the operations of it, if that's okay. Yeah, let's do it. Okay. So how does it work? Like if I am you know if I want to make an extra buck do I just sign up on your website to start you know to put a camera on my on my forehead and start sending you data or how does it work so we actually have a lot of different approaches on the operator side one would be we have a direct consumer approach. And so through direct mobile apps, can you sign up from anywhere in the world?

2. 12:35

   Can you start contributing? Can you register which profession you're in? And can you get streamed a sequence of tasks to do such as cleaning your bedroom, cooking your breakfast, cleaning up afterwards? This is a very lightweight approach to get that diversity ultimately of this pre-training data. The second approach that you have to get into is ultimately there is so much more beyond the home to collect.

3. 13:03

   And so we had to build out a lot of this B2B engine and this muscle of being able to recruit businesses across this incredible taxonomy of all physical human work, which is across bakeries. It could be a PCB manufacturing line, everything to... Podcasting. Podcasting. There's not too much handwork in podcasting just yet. Trying to make a quick buck here. We should have equipped ourselves. and that's ultimately how contributors come into the mix, but the actual data engine for the data factory is so much more sophisticated and much more painful than folks can expect, and that is that a lot of the things that we have to distribute into the world didn't exist until we designed and produced this hardware. Even across tactile gloves is a fantastic example. There is nothing off the shelf that you can purchase and that you can wear. And that reliably tells you how much force you are

4. 13:59

   applying day to day and turn that into ready data sets for these different robotics foundation models. So we have to have that hardware iteration loop to have these three, four different product cycles, that engineering muscle to actually think and iterate what is going to be useful and then scale that production into the world. And that becomes this logistics of distributing into these thousands of homes, thousands of businesses to collect that, to upload that. We've had to set up these 100 gigabyte upload lines in a lot of the places that we operate in just to sustain the hundreds of petabytes of data that we have to deal with at this point. And then you have to process, you have to make sure your quality gates are incredible, and then all of the computer vision to make it useful ultimately. So let's say you decide, okay, in my training mix, there's not