YouSaid · the spoken record

Anca Dragan

lines on the record: 98
first: 2020-03-19
most recent: 2020-03-19
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“It's a very simple way of underactuation where basically there's literally these degrees of freedom that you can control and these degrees of freedom that you can't, but you influence them. And I think that's the important part is that they don't do whatever regardless of what you do, that what you do influence is what they end up doing.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“Kind of the next level. We call this like this underactuated system idea where it's like an underactuated system robotics, but it's kind of You influence these other degrees of freedom, but you don't get to decide what they do.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“On what you do because they don't plan in isolation either, right? So when you see cars trying to merge on a highway and not succeeding, one of the reasons this can be is because they look at traffic that keeps coming. They predict what these people are planning on doing, which is to just keep going. And then they stay out of the way because there's no feasible plan. Any plan would actually intersect with one of these other people. So that's bad. So you get stuck there. So now kind of if you start thinking about it as no, no, no. Actually, these people change what they do depending on what the car does. Like if the car actually tries to kind of inch itself forward, they might actually slow down and let the car in. And now taking advantage of that, well, that”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“Treating it as such is kind of a way we can step outside of this kind of mode that where you try to anticipate what people do and you don't realize you have any influence over it while still protecting yourself because you're understanding that people also understand that they can influence you. And it's just kind of back and forth. It's this negotiation, which is really talking about different equilibria of a game. The very basic way to solve coordination is to just make predictions about what people will do and then stay out of their way. And that's hard for the reasons we talked about, which is have to understand people's intentions implicitly, explicitly, who knows, but somehow you have to get enough of an understanding of that to be able to anticipate what happens next. And so that's challenging. But then it's further challenged by the fact that people change what they're due based.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, I think a little bit, yes, but then this other thing that we can think about is it goes back to what you were saying, that interaction is really game theoretic, right? So the moment you're taking actions in a space, humans are taking actions in that same space, but you have your own objective, which is, you know, you're a car, you need to get your passenger to the destination. And then the human nearby has their own objective, which someone overlaps with you, but not entirely. You're not interested in getting into an accent with each other, but you have different destinations and you want to get home faster and they want to get home faster. And that's a general sum game at that point. And so I think that's what.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“And kind of dots on a screen, and a triangle is chasing the square, and you get really angry at the darn triangle, because why is it not leaving the square alone? So that's, yeah, we can't help. So that was the first thought.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“One is it's we respond to someone being assertive, but we also respond to someone being vulnerable. So I think robots, my first thought is that robots get shoved around and bullied a lot because they're sort of tempting and they're sort of showing off or they appear to be showing off. And so I think going back to these things we were talking about in the beginning of making robots a little more a little more expressive, a little bit more like, ah, that wasn't cool to do. And now I'm bummed. I think that can actually help because people can't help but anthropomorphize and respond to that. Even that, though, the emotion being communicated is not in any way a real thing and people know that it's not a real thing because they know it's just a machine. We're still interpreting, you know, we watch, there's this famous psychology experiment with little triangles.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“Okay. Person has a belief. They see an action. They make some assumptions about how the robot generates its actions, presumably as being rational because robots are rational. It's reasonable to assume that about them. And then they incorporate that new piece of evidence in the Bayesian sense and their belief and they obtain a posterior. And now the robot is trying to figure out what actions to take such that it steers the person's belief to put as much probability mass as possible on the correct parameters.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“Door, and they're like, oh, yeah, the robot's trying to get to that door. So, this thing that we have to do with humans to try to understand their goals and intentions, humans are inevitably going to do that to robots. And then that raises this interesting question that you asked, which is, can we do something about that? This is going to happen inevitably, but we can sort of be more confusing or less confusing to people. And it turns out you can optimize for being more informative and less confusing if you have an understanding of how your actions are being interpreted by the human, how they're using these actions to update their belief. And honestly, all we did is just Bayes rule. Basically,”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“So both, right? So it's really interesting. We've seen a little bit of progress on this problem, on pieces of this problem, so you can, again, it kind of comes down to how complicated is the human model need to be. But in one piece of work that we were looking at, we just said, okay, there's these parameters that are internal to the robot and what the robot is about to do or maybe what objective, what driving style the robot has or something like that. And what we're going to do is we're going to set up a system where part of the state is the person's belief over those parameters. And now when the robot acts, the person gets new evidence about this robot internal state. And so they're updating their mental model of the robot, right? So if they see a card that sort of cuts someone off, they're like, oh, that's an aggressive car. They know more, right? If they see sort of a robot head towards a particular”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“Autonomous car is it about quad rotors or is it about the same underlying principles apply? Of course, when you're trying to get a particular domain to work, you usually have to do some extra work to adapt that to that particular domain. But these things that we were talking about around, well, you know, how do you model humans? It turns out that a lot of systems need to core benefit from a better understanding of how human behavior relates to what people want and need to predict human behavior, physical robots of all sorts and beyond that. And so I used to do manipulation. I used to be, you know, picking up stuff and then I was picking up stuff. with people around and now it's sort of very broad when it comes to the application level but in a sense very focused on okay how does the problem need to change how do the algorithms need to change when we're not doing a role”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, so I tried to find kind of underlying I don't know what to even call them. I try to work on, you know, I might call what I do, the kind of working on the foundations of algorithmic human-robot interaction and trying to make contributions there. And it's important to me that whatever we do is actually somewhat domain agnostic when it comes to is it about, you know,”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“And I think kind of our view recently has been take the Bellman update in AI and just break it in all sorts of ways by saying state. No, no, no. The person doesn't get to see the real state. Maybe they're estimating somehow. Transition function. No, no, no, no, no. Even the actual reward evaluation, maybe they're still learning about what it is that they want. When you watch Netflix and, you know, you have all the things and then you have to pick something. Imagine that the AI system interpreted that choice as this is the thing you prefer to see. How are you going to know? You're still trying to figure out what you like, what you don't like, et cetera. It's important to also account for that. So it's not irrationality. They're doing the right thing under the things that they know.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“That they have. That's right. That's right. It's not too, I mean, there's things in behavioral economics too that, for instance, have touched upon the planning horizon. So there's this idea that there's bounded rationality, essentially. And the idea that, well, maybe we work under computational constraints.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“So it's not conclusive in any way, but I'd say it's evidence that, yeah, maybe we're kind of underestimating humans in some ways when we're giving up and saying, yeah, there's just crazy noisy.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“What we found is that you can actually try to figure out what What physics model kind of best explains human actions? And then you can use that to sort of correct what it is that they're commanding the craft to do. So they might, you know, be sending the craft somewhere, but instead of executing that Where do they think that the craft is going? Where are they trying to send it to? And then you can use the real physics, the inverse of that to actually figure out what you should do so that you do that instead of where they were actually sending you in the real world. And I kid you not at word, people land the damn thing. And, you know, in between the two flags and all that.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“Maybe there's something you're missing, and it's, you know, it especially happens to robots because they're kind of dumb and they don't know things. And oftentimes people are sort of super irrational and that they actually know a lot of things that robots don't. Sometimes like with the Lunar Lander, the robot, you know, knows much more. It turns out that if you try to say, look, maybe people are operating this thing, but assuming a much more simplified physics model because they don't get the complexity of this kind of craft or the robot arm with 70 degrees of freedom with these inertias and whatever. So, maybe they have this intuitive physics model, which is not this notion of intuitive physics is something that is studied actually in cognitive science, which is like Josh Denenbaum, Tom Griffith's work on this stuff.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“Been thinking about is instead of kind of giving up and saying people are too crazy and irrational for us to make sense of them. Maybe we can give them a bit the benefit of the down and maybe we can think of them as actually being relatively rational, but just under different assumptions about the world, about how the world works, about, you know, they don't have, when we think about rationality, implicit assumption is, oh, they're rational under all the same assumptions and constraints as the robot, right? This is the state of the world, that's what they know. This is the transition function, that's what they know. This is the horizon, that's what they know. But maybe kind of this difference, the way the reason they can seem a little messy and hectic, especially to robots, is that perhaps they just make different assumptions or have different beliefs.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“They're messy and complicated, right? I understand. Going back to what we were talking about earlier, right now we're kind of in this dilemma of, okay, there are tasks that we can just assume people are approximately rational for and we can figure out what they want. We can figure out their goals. We can figure out their driving styles, whatever. Cool. There are these tasks that we can. So what do we do, right? Do we pack our bags and go home? And this one, I've had a little bit of hope recently. And I'm kind of doubting myself because what do I know that, you know, 50 years of behavioral economics hasn't figured out? But maybe it's not really in contradiction with the way that field is headed. But basically one thing that”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“And they just need to go straight. It's kind of the same regardless of they're aggressive or defensive. And so you need to enable the robot to reason about how it might actually be able to gather information by changing the actions that it's taking. And then the robot comes up with these cool things where it kind of nudges towards you and then sees if you're going to slow down or not. And if you slow down, it sort of updates its model of you and says, oh, okay, you're more on the defensive side. So now I can actually link.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“And it's very difficult to just think that if you want to hedge your bets and say, ah, maybe they're actually pretty aggressive. I shouldn't try this, you kind of end up driving next to them and driving next to them, right? And then you don't know because you're not actually getting the observations that you get away. Someone drives when they're next to you.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, good, good clarification question. So you have an autonomous car and is trying to navigate the road around human-driven vehicles. Similar things, ideas apply to pedestrians as well, but let's just take human-driven vehicles. So now you're trying to change a lane. Well, you could be trying to infer this driving style of this person next to you. You'd like to know if they're in particular, if they're sort of aggressive or defensive, if they're going to let you kind of go in or if they're going to not.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“Gathering actions that the robot can take to sort of solicit responses that are actually informative. So, for instance, this is not for the purpose of assisting people, but with kind of back to coordinating with people in cars and all of that, one thing that Dorsa did was so we were looking at cars being able to navigate around people. And you might not know exactly the driving style of a particular individual that's next to you, but you want to change lanes in front of them.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, absolutely. So one of the things that's been exciting to me lately is this notion that when you try to, that when you try to think of the robotics problem as, okay, I have a robot and it needs to optimize for whatever it is that a person wants it to optimize as opposed to maybe what a programmer said. That problem, we think of as a human robot collaboration problem in which both agents get to act, in which the robot knows less than the human because the human actually has access to at least implicitly to what it is that they want. They can't write it down, but they can talk about it, they can give all sorts of signals, they can demonstrate, but the robot doesn't need to sit there and passively observe human behavior and try to make sense of it. The robot can act too. And so there's these information.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“You know, pack my bags and go home and switch jobs because it's just, it feels really daunting to make sense of human behavior enough that you can reliably understand what people want, especially as robot capabilities will.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“It's really hard to do that for, say, Lunar Lander because people are all over the place. And so they seem much more noisy than really rational. That's an example of a task where these models are kind of failing us. And it's not surprising because we talked about the 40s, utility, late 50s, sort of noisy. Then the 70s came and behavioral economics started being a thing where people were like, no, no, no, no, no. People are not rational. People are messy and emotional and irrational and have all sorts of heuristics that might be domain specific and they're just a mess. So, what do you so what does my robot do to understand what you want? And it's very, that's why it's complicated. For the most part, we get away with pretty simple models until we don't. And then the question is, what do you do then? I had days when I wanted”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“So imagine you're trying to control some robot that's fairly complicated. You're trying to control a robot arm because maybe you're a patient with a motor impairment and you have this wheelchair mounted arm and you're trying to control it around. Or one test that we've looked at with Sergei is, and our students did, is lunar lander. So I don't know if you know the Satari game. It's called Lunar Lander. It's really hard. People really suck at landing the same. Mostly they just crash it left and right. Okay, so this is the kind of task. Imagine you're trying to provide some assistance to a person operating such a robot where you want the kind of the autonomy to kick in, figure out what it is that you're trying to do and help you do it.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“We had Luce and Shepherd come in and say people are a little bit noisy and approximate in that process. So they might choose something kind of stochastically with probability proportional to how much utility something has. There's a bit of noise in there. This has translated into robotics and something that we call Boltzmann rationality. So it's a kind of an evolution of inverse reinforcement learning that accounts for human noise. And we've had some success with that too for these tasks where it turns out people act noisily enough that you can't just do vanilla, the vanilla version. Ah, you can account for noise and still infer what they seem to want based on this. Then now we're hitting tasks where that's not enough.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“Whatever it is that people want, right? So you make that assumption, and now you can kind of inverse, or that's why it's called inverse, well, really optimal control. Also, inverse reinforcement learning. So, this is based on Utility maximization in economics, right? Back in the 40s von Neumann Morgenstein were like, okay, people are making choices by maximizing utility. Go. And then in the late 50s,”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“Briefly said it. Right. So it's the problem of take human behavior and infer reward function from this. Figure out what it is that that behavior is optimal with respect to. And it's a great way to think about learning human preferences in the sense of, you know, you have a car and the person can drive it and then you can say, well, okay, I can actually learn what the person is optimizing for. I can learn their driving style or you can have people demonstrate how they want the house clean. And then you can say, okay, this is, I'm getting the trade-offs that they're making. I'm getting the preferences that they want out of this. And so we've been successful in robotics somewhat with this. And it's based on a very simple model of human behavior. It was remarkably simple, which is that human behavior is optimal with respect to.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“Because I think that we're not arbitrary. We make these decisions that we make. We act in the way we do because we're trying to achieve certain things. And so I think that's the relationship between them. Now, how complicated do these models need to be in order to be able to understand what people want? We've gotten a long way in robotics with something called inverse reinforcement learning, which is the notion of someone acts demonstrates what how they want the thing done.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“Why is this hard? Yeah. Why is understanding humans hard so? I think There's two tasks about understanding humans that in my mind are very, very similar, but not everyone agrees. So there's the task of being able to just anticipate what people will do. We all know that cards need to do this, right? We all know that, well, if I navigate around some people, the robot has to get some notion of, okay, where is this person going to be? So that's kind of the prediction side. And then there's what you are saying, satisfying the preferences, right? So adapting to the person's preferences, knowing what to optimize for, which is more this inference side, this what is, what does this person want? What is their intent? What are their preferences? And to me, those kind of go together because I think that at the very least, if you can understand, if you can look at human behavior and understand what it is that they want, then that's sort of the key enabler to being able to anticipate what they'll do in the future.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“Changing the problem in two ways. The first way it changes the problem is that the robot is no longer the single agent acting. That you have humans who also take actions in that same space, you know, cars navigating. And then there's problem number two, which is Goes back to this notion of if I'm a programmer, I can specify some objective for the robot to go off and optimize and specify the task. But if I put the robot in your home, presumably you might have your own opinions about, well, okay, I want my house clean, but how do I want it cleaned and how should robot move, how close to me it should come and all of that? And so I think those are the two differences that you have. You're acting around people and what you should be optimizing for should satisfy the preferences of that end user, not of your programmer who programmed you.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“And then human robot interaction, and then talk about how that, yeah. So, and by the way, I'm going to talk about this very particular view of human robot interaction, right, which is not so much on the social side or on the side of how do you have a good conversation with the robot? What should the robot's appearance be? It turns out that if you make robots taller versus shorter, this has an effect on how people act with them. I'm not talking about that, but I'm talking about this very kind of narrow thing, which is you take, if you want to take a task. That a robot can do in isolation in a lab out there in the world, but in isolation. And now you're asking, what does it mean for the robot to be able to do this task for presumably what its actually end goal is, which is to help some person. That ends up.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“But it's really hard because it, I don't know, you know, the way I would think about it and the way I thought about it when it came to expressing goals or intentions for robots, it's, well, what's really happening is that instead of doing robotics where you have your state and you have your action space and you have your reward function that you're trying to optimize, now you kind of have to expand the notion of state to include this human internal state. What is the person actually perceiving? What do they think about the robots? Something rather. And then you have to optimize in that system. And so that means you have to understand how your motion, your actions, and dope sort of influencing the observer's kind of perception of you. And it's very hard to write math about that.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“Kids are being rude to Alexa because they can be rude to it and it doesn't really get angry, right? It doesn't reply in any way. It just says the same thing. So I think there's at least for that, for the correct development of children, that these things you kind of react differently. I also think, you know, you walk in your home and you have a personal robot. And if you're really pissed, presumably the robot should kind of behave slightly differently than when you're super happy and excited.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“Versus it's hesitant versus, you know, maybe it's happy or it's disappointed about something, some failure that it had. Or I think that when robots move. They can communicate so much about internal states or perceived internal states that they have. And I think that's really useful and an element that we'll want in the future because I was reading this article about how Kids”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“Really hard, right? So it depends on what setting. So if you want to do it in this very particular narrow setting where it does only one thing and it's expressive, then you can get an animator. You can have pixel on call come in, design some trajectories. There was a Anki had a robot called Cosmo where they put in some of these animations. That part is easy, right? The hard part is doing it not via these kind of handcrafted behaviors, but doing it generally autonomously. I want robots. I don't work just to clarify. I used to work a lot on this. I don't work on that quite as much these days. But the notion of having robots that, you know, when they pick something up and put it in a place, they can do that with various forms of style. Or you can say, well, this robot is succeeding at this task and is confident.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“A special connection to Wally. Long story short, I like Wally because I like animation and I like robots, and I like, you know, the fact that this was, we still have this robot to this day.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, it has These cameras. And they move, so yeah, that's it's, you know, it goes, and then it's super cute. Yeah, it's, you know, the way it moves is just so expressive, the timing of that motion, what it's doing with its arms and what it's doing with these lenses is amazing. And so I've really liked that from the start. And then on top of that, sometimes I share this, it's a personal story I share with people or when I teach about AI or whatnot. husband proposed to me by building a walleye. And he actuated it so it's 70 degrees of freedom, including the lens thing. And it kind of came in and it had the, he made it have like a, you know, the belly box opening thing. So it just did that. And then it's spewed out this box made out of Legos that open slowly and then bam.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“It was on the Firefly there. I think that was the Lexus, by the way. This was back then. But yeah, so good question. Okay, my favorite fictional robot is Wally. And I love how amazingly expressive it is. I'm a person who thinks a little bit about expressive motion kinds of things you're saying, but you can do this and it's a head and it's the manipulator. And what does it all mean?”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“I did a little bit of the computer science Olympian, but not as serious as I did the math Olympian. So it was programming. Yeah, it's basically here's a hard math problem solving with a computer was kind of the deal.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“Okay, the first program I've written I kind of do. It was in QBASIC in fourth grade. And it was drawing like a circle. Yeah. I don't know how to do that anymore, but in fourth grade. That's the first thing that they taught me. I was like, you could take a special. I wouldn't say it was an extra curate, isn't a sense an extracurricular, so you could sign up for dance or music or programming. And I did the programming thing and my mom was like, what? Why?”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“I got into that fairly early and it was a little. Maybe to just theory would now kind of, I didn't kind of have a goal. And other than understanding, which was cool, I always like learning and understanding, but there was no, okay, what am I applying this understanding to? And so I think that's how I got into more heavily into computer science because it was kind of math meets something you can do tangibly in the world.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, the really cute one. Yeah. And I put it on my laptop and I had that for years until I finally changed my laptop out. And, you know.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“That was, I remember that experience very viscerally writing in that car and being just wowed, I had the, they gave us a sticker that said I wrote in a self-driving car and I had this cute little firefly on. And or logo, or something like that.”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“Which is, I, you know, I used to do mostly manipulation in my PhD, but now I do kind of a bit of everything application-wise, including cars. And I got into cars because I was here in Berkeley while I was a PhD student still for RSS 2014. Peter Bill organized it. And he arranged for, it was Google at the time to give us rides in self-driving cars. And I was in a robot. And it was just making decision after decision the right call. And it was so amazing. So it was a whole different experience, right? Just, I mean, manipulation is so hard. You can't do anything. And there it was. Was it the most...”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source
“Think it was a very gradual process, and it was somewhat accidental actually because I first started getting into programming when I was a kid and then into math and then into, I decided computer science was the thing I was going to do. And then in college, I got into AI. And then I applied to the robotics institute at Carnegie Mellon. And I was coming from this little school in Germany that nobody had heard of. But I had spent an exchange semester at Carnegie Mellon. So I had letters from Carnegie Mellon. So that was the only place, you know, MIT said no. Berkeley said no. Stanford said no. That was the only choice I got into. So I went there to the robotics institute. And I thought that robotics is a really cool way to actually apply the stuff that I knew and love, like optimization. So that's how I got into robotics. I have a better story how I got into”
2020-03-19 · Lex Fridman Podcast · #81 – Anca Dragan: Human-Robot Interaction and Reward Engineering · IDENTIFIED FROM THE TRANSCRIPT · source