00:14:16 · Karol Hausman
[00:14:16] S0: of philosophers were contemplating the nature of God and the nature of reality. And there was often this idea that God is this separate entity that either was the creator of everything or had some kind of way of controlling everything that is that is happening in the universe. And I think with with him, what was what was so interesting was that he said that everything around us, like all of reality, that is God. That is that is what it is. And there is some underlying structure to it, and that structure in it itself is where the beauty lies and where the where the intelligence lies. And I and I thought that was very thought provoking. And as you study physics and, you know, things like robotics or AI, I think more and more you realize that there's some underlying structure to things. And in some ways, that structure is quite counterintuitive. Like, why why it's there? Why is it so that you can describe all the complexity of the world in a simple equation rather than in a million different equations? And the the more beautiful and short that equation is, the more real it seems or the more accurate it is. And I thought this was kind of the closest to what it feels like today given the current state of science.
00:15:35 · Mario Gabriele
[00:15:35] S1: You had a tweet where you were talking about a book that I tried to read as much of it as I could in preparation for this called The Inner Game of Tennis. And you sort of mentioned how... I mean, you were really talking about tennis, but it seemed like some deep parallels to robotics. You sort of make the point that they talk about the fact in this book that if you really focus with your conscious mind on improving your stroke, you could sort of get to this local maximum where maybe you are doing a little bit better at hitting a forehand, but really what you need to do to get to an optimal stroke is sort of allow the unconscious mind to to take over in some respect. How do you think about emulating this aspect of almost the unconscious and the role it plays in fluid motion and, I don't know, proprioception and all these sort of things when it comes to robotics. Is that... Were were you thinking about robotics when you were reading that book?
00:16:33 · Karol Hausman
[00:16:33] S0: A lot. Yeah. Definitely, I was. I don't think it just applies to motion. I think it's... There's a big difference between how we learn things and how we think we learn things or how we teach things to others. You know, even if you look at something like learning a language, the way we we thought you're supposed to teach it or the way we would wanna teach it to to others in school is by defining all the rules and, first learn about grammar and all the different concepts and how tenses work and what word follows what other words. So you learn all of that structure so that the thinking is that if you know all of the structure, can follow those rules and then speak like a native speaker would. But that's not how we learn language. Right? Like you're just immersed in it. You just hear everybody using the structure that they kind of intuitively know, And then all of a sudden you emerge with a full understanding of how language works even though you can't really pinpoint any of these rules. There are many native speakers that speak perfectly, follow all the rules exactly, but have no idea what these rules are, what the underlying structure is, what declination is, what tenses are. They have no idea of any of that, but they will follow them perfectly. With that book, I think that was that was shown, but in the in the in the in in the motion aspect of things where, you know, you could you could read all you want about how to play tennis, or you can try to describe, you know, where your elbow should be exactly where you hit the forehand or how to adjust your swing. But the only way to really learn it is by just doing it and kind of immersing yourself yourself in it. And I think there is a lot of parallels to the robotics world and and to the
00:18:15 · Mario Gabriele
[00:18:15] S1: world of AI, how we how we thought that the machines should think or how they should learn things versus how they actually learn them. You... I think in 2023, say that you had a moment where for the first time you really felt like you could see a bit of the future of the robotics industry, and things were really clicking into place. But you had really been in that field for quite a while already, it strikes me. So I wondered what the period before that moment was like. Like, was that a quite a lonely time to be working in a space where you don't know how it's gonna play out?
00:18:50 · Karol Hausman
[00:18:50] S0: So maybe I can go back and tell you a little bit more about the journey. I would love that. So you understand that that kind of the feeling. After I moved to Warsaw, as I as I mentioned, it it was very disappointing to see the state of robotics. And it... For a long time, it felt like no one was working on the intelligent robots that I really wanted to work on. And I remember at the time, I was searching for for really anything, anybody who who is doing something that that is a little bit closer to what I imagined robots to be. And for a long time, it felt like no one was. Then I remember I I finally found someone. I found a professor in Germany that was that was writing papers that seemed a little bit closer to what I would imagine robots to be. And there was this this master's program in in Munich called robotics, cognition, and intelligence. And I thought if I if I apply for this program and get in and they don't do intelligent robots, then I'm basically lost. Like, if the program with with that title doesn't do anything that I that I imagined, then something is very, very wrong. So I applied and and moved to Munich. And I remember the first day of classes, I went to one of the the the... One of the very first classes, and I got stuck in a in a metro somewhere and then was super light, so I missed the entire class. And I and I showed up. No one was there. The class was over at that point. But I saw a janitor, and I was looking for a for a bathroom. So I asked him where the bathroom is. He pointed me to the to the 3rd Floor of the building. I remember it very vividly. And as I went upstairs, I remember seeing these big, big doors, and there was some mechanical sound coming from behind them. I don't know why, but I just decided to, like, open them and see what's going on there, that the sound was very intriguing. And as I open these doors, I see these two basically humanoid robots doing... One of them was making popcorn and the other one I believe was spreading butter on a toast. And that was the moment where, you know, this is something I've been searching for at this point for for years. So that was the moment that that was probably one of the most powerful moments I I had during that journey where I finally found it. There's robots that that look intelligent, that do something that is very human like. There are people who work on this.
00:21:09 · Karol Hausman
So I immediately left the room, knocked because there's basically no one there. And I knocked at the at the at the room next door and asked if I could work there. And, basically, I was completely unqualified. Had no idea how to program these robots. I was not familiar with any of the tools, but the person who was there, it was another one of those people that gave me a chance and and asked me to just come in on Monday and see what I could do. Wow. So I got involved, got a job at that at that lab. That's how I got into finally working on intelligent robots. So, yeah, I I think this is like yet another person that I'm really grateful to. And then afterwards, I I moved to The US, got into a PhD program studying studying intelligent robots. And at that point, I was so stoked that, like, I finally get to do this and I finally found a small group of people that work on those things. It didn't matter that, you know, it doesn't work that well. I was so excited that I finally get to, you know, meet the people that are that are working on this. I can learn from them. I can I can push it forward, and and I can finally work on the thing that I always wanted to work on? So I did this throughout my PhD, and I had another moment like this during my PhD, this kind of defining moment where I was working on a certain set of problems where it was referred to as as active perception. And I can I can go a little bit deeper into this if you're interested? But at that point, it kinda felt like there are ways to write... To to to write papers, to to progress in your PhD, but there isn't anything that felt like it could actually solve this problem. It it all felt like the world is too complex. You can push it to some extent, this technology, but you you won't really get to the finish line. They're not they're not kind of adding up. You don't really see a path out of it. And there was another moment where a poster candidate stopped by our lab. And at that time, when a poster candidate candidate stops by, you are supposed as a PhD student, show them your work and kind of talk with them as part of the interview. So I showed to that post that candidate stuff that I was working on, and he listened to all of it. And then at the end of it, he said that I should drop all of this and switch to deep learning. That's the way to to solve the problem that I was actually working on.