Building the factories of the future with Covariant CEO Peter Chen

**Sarah** (0:06)
Hi, listeners. Welcome to another episode of No Priors. This week, I'm joined by Peter Chen, the co-founder and CEO of Covariant, a robotics startup that is developing AI robots.
Before he started Covariant, Peter was a research scientist at OpenAI, and a researcher at the Berkeley AI Research Lab, where he focused on reinforcement learning, meta-learning, and unsupervised learning. He is a prolific publisher, and now a founder. I'm so excited to have you on today to talk about what's going on in robotics. Welcome, Peter.

**Peter Chen** (0:35)
Thanks, Sarah. It's great to be here.
There are many exciting reasons to be here. One is I have been a frequent listener of the podcast, and the second one is just because of the name, I just have to be on this show. It's great to be here.

**Sarah** (0:50)
Right. Let's go establish some priors for everybody in a very unknown landscape.
Can we start with just why you were drawn to robotics and the beginning of your research journey?

**Peter Chen** (1:03)
Yeah. When I was working on research at both UC Berkeley as part of my PhD and at OpenAI, there were two topics that were particularly exciting to me. One topic is, as you have introduced, unsupervised learning, like how can we build models that learn from vast amount of data.
We now more colloquially known this as generative AI, because we train this large models on large amount of text, images, videos, and you learn from them in an unsupervised manner. That topic has always been very interesting to me, because if you want to train very capable AI's, you want to have a lot of data. Where you can get a lot of data is through this unsupervised dataset.
Then the second topic that was really interesting to me was reinforcement learning. It's not just building models that understand, but building models that can make decisions.
Reinforcement learning teach these models to make decisions by having them make trials and errors and learn from the better decisions and do less of the worst decisions. Robotics is just such a great combination of these fields. In order to be really capable robots, they need to really understand the world in a very robust way. They are not just passive agents that just understand text or what's in an image, they actually need to take actions in the real world and the consequences do matter. 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 trained on browsing the Internet, but AI that is trained with physical interactions with the world. And so we also believe robotics would be a key way to advance AI.

**Sarah** (2:53)
That makes sense. You were at places that are great places to do research.
Why did you decide to start a commercial company?

**Peter Chen** (3:02)
It's a really good question. I mean, there are a lot of companies that are founded by prior PhDs that are kind of the classic journey of there's a technology that was built in a lab environment and it got to enough level of maturity that we should start to commercialize it in the real world. That was kind of not the journey of Covariant. When we started Covariant, there was not AI that was good enough to make robots do useful things commercially.
And so it was not a classic journey of technology development in academia and then transition to a commercial landscape. The key insight that we had at that time when we left OpenAI in 2017 to start Covariant was the future of AI is going to be the future of foundation models. These models that are truly multi-task, learn from large amount of data and as such be more generalizable. They can solve new tasks more easily and are also more capable at every single one of the tasks because of the transfer that you get across tasks.
We just had early conviction that that was the path to build AI, and that is also going to be true for the physical world, for robotics.
But there's one big problem, which is you have no data set to build robotics foundation model. There's no data set that you can build this AI that understands the physical world and take actions in the physical world. In order to build this foundation models for robotics, you really have to build a company that can collect data to do it. And the only way to collect enough data is to build fleets of robots that are actually creating value for customers, 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 limit on how much you can do that. In that perspective, we strongly believe in the Tesla approach, where they have the most self-driving car data because they ship great cars that people want to drive and a good enough entry-level autopilot that people are willing to use it. And they're creating value for their customers, like customers use their products. And those data that they collect can allow them to build much more capable models and AI. And so why we left OpenAI and academia to start Covariant is very much this belief that in order to build foundation models for robots, you have to have a lot of data. And in order to have a lot of data, you have to build autonomously working systems for customers. And the only way to do that is to build a company to serve those customers.