YouSaid · the spoken record

Vijay Kumar

lines on the record: 68
first: 2019-09-08
most recent: 2019-09-08
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 always in the context of society. As engineers, we cannot afford to lose sight of that. So I think that's important. But I think one thing that people underestimate when they do robotics is the importance of mathematical foundations, the importance of representations. Not everything can just be solved by looking for ROS packages on the internet or to find a deep neural network that works. I think the representation question is key, even to machine learning, where if you ever hope to achieve or get to explainable AI, somehow there need to be representations that you can understand.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“I think there's really three things. One is you have to get used to the idea that the world will not be the same in five years or four years whenever you graduate, right? Which is really hard to do. So this thing about predicting the future, every one of us needs to be trying to predict the future always. Not because you'll be any good at it, but by thinking about it, I think you sharpen your senses and you become smarter. So that's number one. Number two, And it's a corollary of the first piece, which is you really don't know what's going to be important. So this idea that I'm going to specialize in something which will allow me to go in a particular direction, it may be interesting, but it's important also to have this breadth so you have this jumping off point. I think the third thing, and this is where I think Pen excels. I mean, we teach engineering, but it's always in the context of the liberal arts.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“Think so, and we don't have a systematic way of understanding that. Know everybody says just because a computer can now beat a human at any board game, we suddenly know something about intelligence. That's not true. A computer board game is very, very structured. It is the equivalent of working in a Henry Ford factory where things parts come, you assemble, move on. It's a very, very, very structured setting. That's the easiest thing, and we know how to do that.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“I think there are lots of problems, right? But I would phrase it in the following way. If you look at the robots who are building, they're still very much tailored towards doing specific. I think the question of how do you get them to operate in much broader settings. Where things can change in unstructured environments is up in the air. So think of a self-driving cars. Today, we can build a self driving car in a parking lot. We can do level five autonomy in a parking lot. But can you do level five autonomy in the streets of Napoli in Italy or Mumbai in India? No. So, in some sense, when we think about robotics, we have to think about where they're functioning, what kind of environment, what kind of a task. We have no understanding of how to put both those things together.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“Absolutely. I think the days where politicians can be agnostic to technology are gone. I think every politician needs to be literate in technology. And I often say technology is the new liberal art. Understanding how technology will change your life, I think, is important and every human being needs to understand that.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“If somebody had to develop this technology, wouldn't you rather the good guys do it? So the good guys have a good understanding of the technology so they can figure out how this technology is being used in a bad way or could be used in a bad way and try to defend against it. So we think a lot about that. So we're doing research on how to defend against swarms, for example. There's in fact a report by the National Academies on counter UAS technologies. This is a real threat, but we're also thinking about how to defend against this and knowing how swarms work, knowing how autonomy works is, I think, very important.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“Anytime we develop a technology meaning to have positive impact in the world, there's always the worry that. Somebody could subvert those technologies and use it in an adversarial setting. And robotics is no exception, right? So I think it's very easy to weaponize robots. I think we talk about swarms. One thing I worry a lot about is, so for us to get swarms to work and do something reliably is really hard. But suppose I have this challenge of trying to destroy something and I have a swarm of robots where only one out of the swarm needs to get to its destination. So that certainly becomes a lot more doable. And so I worry about this general idea of using autonomy with lots and lots of agents. I mean, having said that, look, a lot of this technology is not very mature. My favorite saying is that”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“And I think the robots can do a very good job of modeling humans if you really think about the framework that you have, a human sitting in a cockpit surrounded by sensors all staring at him, in addition to being staring outside, but also staring at him. I think there's a real synergy there.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“And oftentimes telling you to drive to a certain place, although you have no intention of going there, because it thinks that that's where you should be because of some Gmail calendar entry or something like that. And it's trying to constantly figure out who you are, what you're doing. If a car were to do that, maybe that would make the driver safer because the car is trying to figure out, is the driver paying attention, looking at his or her eyes, looking at circadic movements. So I think the potential is there, but from the reverse side, it's not robot modeling, but it's human modeling.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“In some sense, you know, we're also doing this, you think about the human driving a car and almost invariably the humans trying to estimate the state of the car, they estimate the state of the environment, so on. But what is the car were to estimate the state of the human? So for example, I'm sure you have a smartphone and the smartphone tries to figure out what you're doing and send you reminders.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“Got to do something. It's important. I have to do it in two minutes. The building is burning. There's been an explosion. It's collapsed. How do I do it? I think to me, those are the interesting things where it's very, very unstructured. And what's the role of the human? What's the role of the robot? Clearly there's lots of interesting challenges and as a field, I think we're going to make a lot of progress in this area.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“This is harder than it sounds. I think. You know, if you I'm sure you've driven before in highways and so on. It's really very hard to have to relinquish control to a machine and then take over when needed. So I think Tesla's approach is interesting because it allows you to periodically establish some kind of contact with the car. Toyota, on the other hand, is thinking about shared autonomy or collaborative autonomy as a paradigm. If I may argue these are very, very simple ways of human robot collaboration because the task is pretty boring. You sit in a vehicle, you go from point A to point B. I think the more interesting thing to me is, for example, search and rescue, I've got a human first responder, robot first responders.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“And third, we think about humans as bystanders. Self-driving cars, what's the human's role, and how do self-driving cars acknowledge the presence of humans. So I think all of these things are different scenarios. It depends on what kind of humans, what kind of tasks. And I think it's very difficult to say that there's a general theory that we all have for this. But at the same time, it's also silly to say that we should think about robots independent of humans. So to me, human-robot interaction is almost a mandatory aspect of everything we do.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“I don't think we should ever think about robots without human in the picture. Ultimately, robots are there because we want them to solve problems for humans. But there is no general solution to this problem. I think if you look at human interaction and how humans interact with robots, we think of these in sort of three different ways. One is the human commanding the robot. The second is a human collaborating with the robot. So, for example, Robot can actually pick up things with a human and carry things. That's true collaboration.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“And again, for short distances, you can do it, and there's no reason to suggest that these are all just have to be rotorcrafts. You take off vertically, but then you morph into a forward flight. I think there are a lot of interesting designs. The question to me is, are these economically viable? And if you agree to do this with fossil fuels, it immediately becomes viable”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“So, look, there are a lot of smart people working on this, and you never say something is not possible when you have people like Sebastian Thrun working on it. So I totally think it's viable. I question again the electric piece.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“Look, there are a lot of companies that are promising flying cars. Are autonomous and that are clean. Think they're overpromising. The autonomy piece is doable. The clean piece, I don't think so. There's another company that I work with called Jatoptra. They make small jet engines. They can get up to 50 miles an hour very easily and lift 50 kilos. But they're jet engines, they're efficient. They're a little louder than electric vehicles, but they can build flying cars.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“The story is the second problem, but storage limits the range. But you have to remember that you have to burn a lot of it per given time.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“The one thing I'll tell you that nobody thinks about is the fact that we've not made huge strides in battery technology. Yes, it's true. Batteries are becoming less expensive because we have these mega factories that are coming up, but they're all based on lithium-based technologies. And if you look at the energy density and the power density, those are two fundamentally limiting Numbers. So power density is important because for a UAV to take off vertically into the air, which most drones do. They don't have a runway, you consume roughly 200 watts per kilo at the small size. That's a lot In contrast, the human brain consumes less than 80 watts. The whole of the human brain. So just imagine just lifting yourself into the air. Is like two or three light bulbs, which makes no sense to me.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“I think there's a lot of potential for the last mile delivery. And so, in crowded cities, I don't know if you go to a place like Hong Kong, just crossing the river can take half an hour. And while a drone can just do it in five minutes at most, I think you look at delivery of supplies to remote villages. I work with a nonprofit called Wi Robotics. So they work in the Peruvian Amazon, where the only highways are rivers. And to get from point A to point B may take five hours, while with a drone, you can get there in 30 minutes. So Delivering drugs, retrieving samples for testing vaccines. I think there's huge potential here. So I think the challenges are not technological. The challenge is economical.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“So that's the downside. I think in three dimensional space, you're modeling three dimensional world, not just because you want to avoid it, but you want to reason about it and you want to work in that three-dimensional environment. And that's significantly harder. So that's one disadvantage. I think the second disadvantage is, of course, anytime you fly, you have to put up with the peculiarities of aerodynamics Their complicated environments. How do you negotiate that? So that's always a problem.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“That's a really interesting question. I think Autonomous flight has several advantages that autonomous driving doesn't have. So, look, if I want to go from point A to point B, I have a very, very safe trajectory. Go vertically up to a maximum altitude, fly horizontally to just about the destination, and then come down vertically. This is pre programmed. Equivalent of that is very hard to find in a self driving car world because you're on the ground, you're in a two-dimensional surface, and the trajectories on the two-dimensional surface are more likely to encounter obstacles. I mean this in an intuitive sense, but mathematically true. Mathematically as well, that's true.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“Literally power, literally power. So in 2014, five years ago, and I don't have more recent data, 2% of US electricity consumption From data farms So we think about this as an information science, an information processing problem. Actually, it is an energy processing problem. And so unless we figure out better ways of doing this, I don't think this is viable.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“Hard to fix linear, totally, but I think it's exponential. The other thing you have to think about is that this process is a very, very power hungry process to run data forms or servers.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“So look, if you have a corner case and your algorithm doesn't work, your instinct is to go get data about the corner case and patch it up, learn how to deal with that corner case. But at some point, This is going to saturate. This approach is not viable. So today, computer vision algorithms can detect 90% of the objects or can detect objects 90% of the time, classify them 90% of the time. Cats on the Internet, I probably can do 95%. But to get from 90% to 99%, you need a lot more data. And then I tell you, well, that's not enough because I have a safety critical application. I want to go from 99% to 99.9%. That's even more data. So I think if you look at Wanting accuracy on the x axis. And look at the amount of data on the y axis. I believe that curve is an exponential curve.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“So we also have a spin off company, ExxonTechnologies, that works underground in mines. So, you go into mines, they're dark, they're dirty. You fly in a dirty area. There's stuff you kick up by the propellers. The downwash kicks up dust. I challenge you to get a computer vision algorithm to work there. So we used LiDARs in that setting. Indoors and even outdoors when we fly through fields, I think there's a lot of potential for just solving the problem using computer vision alone. But I think the bigger question is can you Actually, solve, or can you actually identify all the corner cases using a single sensing modality and using learning alone?”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“In the laboratory, this is the holy grail. Can you do end to end learning? Can you go from pixels to motor currents? This is really, really hard. And I think if you go forward, the right way to think about these things is data-driven approaches, learning-based approaches, in concert with model-based approaches, which is the traditional way of doing things. So I think there's a role for each of these methodologies.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“And so this was there way back. I think what's interesting is if you look at autonomous vehicles today, Learning occurs, could occur in two pieces. One is perception, understanding the world. Second is action taking actions. Everything that I've seen that is successful is on the perception side of things. So in computer vision, we've made amazing strides in the last 10 years. So recognizing objects, actually detecting objects, classifying them and tagging them in some sense, annotating them. This is all done through machine learning. On the action side, on the other hand, I don't know if any examples where there are fielded systems where we actually learn the right behavior.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“Plus, to get everything right would be awfully tedious. So the way we do this is over time we figure out how to adapt to these conditions. So early on, we used a form of learning that we call iterative learning. So this idea of if you want to perform a task, there are a few things that you need to change and iterate over a few parameters that over time you can figure out. So I could call it policy gradient reinforcement learning, but actually it was iterative learning.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“I wouldn't say they're impossible to model, but the level of sophistication you would need in the model and the software would be tremendous.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“And so the models rely on the fact on an assumption that they're actually rigid. But that's not true. If you're flying really quickly, these effects become significant. If you're flying close to the ground, you get pushed off by the ground, something which every pilot knows when he tries to land or she tries to land. This is called a ground effect. Something very few pilots think about is what happens when you go close to a ceiling, well, you get sucked into a ceiling. There are very few aircrafts that fly close to any kind of ceiling. Likewise, we go close to a wall, there are these wall effects. And if you've gone on a train and you pass another train that's traveling in the opposite direction, you feel the buffeting. And so these kinds of microclimates Affect our UAV significantly.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“So let me disagree a little bit with you. I think we never perhaps called out, in my work, called out learning, but even this very simple idea of being able to fly through a constrained space. The first time you try it, you might get it wrong if the task is challenging. And the reason is Get it perfectly right, you have to model everything in the environment. And flying is notoriously hard to model. There are aerodynamic effects that we constantly discover even just before I was talking to you, I was talking to a student about how blades flap when they fly. And that ends up changing how a rotorcraft is accelerated in the angular direction.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“And so for us, it's about finding smooth motions, motions that are safe. So we think about these two things. One is optimality, one is safety. Clearly, you cannot compromise safety. So you're looking for safe optimal motions. The other thing you have to think about is can you actually compute a reasonable trajectory in a small amount of time because you have a time budget. So the optimal becomes suboptimal. But in our lab, we focus on synthesizing smooth trajectory that satisfy all the constraints. In other words, don't violate any safety constraints and is as efficient as possible. And when I say efficient, it could mean I want to get from point A to point B as quickly as possible. Or I want to get to It as gracefully as possible, or I want to consume as little energy as possible.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“I think planning is a very fundamental problem in robotics. I think 10 years ago it was an esoteric thing, but today with self-driving cars, everybody can understand this basic idea that a car sees a whole bunch of things and it has to keep a lane or maybe make a right turn or switch lanes. It has to plan a trajectory. It has to be safe. It has to be efficient. So everybody's familiar with that. That's kind of the first step that you have to think about when you say autonomy.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“On a sphere, if you will. So you can imagine a symmetric configuration. And so you should be able to fly anywhere. But the real challenge we had is the strength-to-weight ratio is not enough. And of course, we didn't have the sensors and so on. So everybody knew, or at least the people who worked with rotocrafts knew four rotors will get it done. So that was not our idea. But it took a while before we could actually do the onboard sensing and the computation that was needed for the kinds of agile maneuvering that we wanted to do in our little aerial robots. And that only happened between 2007 and 2009 in our lab.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“Quadrotors to fly without much success. I said, we've been working on this since 2000. Our first designs were, well, this is way too complicated. Why not we try to get an omnidirectional flying robot? So our early designs, we had eight rotors. And so these eight rotors were arranged uniformly”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“Firstly, this is not the four motor configuration is not ours. It has at least a hundred year history. And various people try to get”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“There's six degrees of freedom, but you only have four inputs, the four motors, and it turns out to be a remarkably Versatile configuration. You think at first, well, I only have four motors. How do I go sideways? But it's not too hard to say, well, if I tilt myself, I can go sideways. And then you have four motors pointing up. How do I rotate in place about a vertical axis? Well, you rotate them at different speeds and that generates reaction moments and that allows you to turn. So it's actually a pretty, it's an optimal configuration from an engineer's standpoint. It's very simple, very cleverly done and very versatile.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“Example, when we walk, we implicitly have this information because we kind of know how what our stride length is. We also are looking at images fly past our retina, if you will. And so we can estimate velocity. We also have accelerometers in our head. And we're able to integrate all these pieces of information to determine where we are as we walk. And so robots have to do something very similar. You need an IMU. some kind of a camera or other sensor that's measuring velocity. And then you need some kind of a global reference frame if you really want to think about doing something in a world coordinate system. And so how do you estimate your position with respect to that global reference frame? That's important as well.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“So I'll come back to that. So in 2007, 2008, we were able to build these. And then the question you're asking was a good one. How do you coordinate the motors? To develop this, but over the last 10 years, everything is commoditized Kit and build us all the low level functionality is all automated. But basically at some level, you have to drive the motors at the right RPMs, the right velocity. In order to generate the right amount of trust, in order to position it and orient it in a way that you need to in order to fly. The feedback that you get is from onboard sensors and the IMU is an important part of it. The IMU tells you what the acceleration is, as well as what the angular velocity is. And those are important pieces of information. In addition to that, you need some kind of local position or velocity information.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“And that's why research is very hard to predict the outcomes. And again, the federal government spent a ton of money on things that they thought were useful for resonators, but it ended up enabling the small UAVs. Which is great because I could have never raised that much money until sold this project hey, we want to build these small UAVs. Can you actually fund the development of low cost IMUs?”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“But they didn't anticipate this boom in IMUs. But if you look subsequently, what happened is that every car manufacturer had to put an airbag in, which meant you had to have an accelerometer on board. And so that drove down the price-to-performance ratio. Oh, that differs.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“So, this is an interesting question. We've been trying to do this since 2000. It is a commentary on the senses that were available back then, the computers that were available back then. A number of things happened between 2000 and 2007. One is the advances in computing, which is, and so we all know about Moore's Law, but I think 2007 was a tipping point, the year of the iPhone, the year of the cloud. Lots of things happened in 2007. But going back even further, inertial measurement units as a sensor really matured. Again, lots of reasons for that. Certainly there's a lot of federal funding, particularly DARPA in the US”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“Actually, it's very interesting because the TED curator, Chris Anderson, told me. Can't show math. And you know, I thought about it, but that's who I am. I mean, that's our work. And so I felt compelled to give the audience a taste for at least some math.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“So, in those kinds of settings, you do need that agility. Agility does not necessarily mean you break records for the 100 meters dash. What it really means is you see the unexpected and you're able to maneuver in a safe way and in a way that gets you the most information about the thing you're trying to do.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“So I used the word agility mostly, or at least we're motivated to do agile robots mostly because Robots can operate and should be operating in constrained environments. And if you want to operate the way a global hawk operates, I mean, the kinds of conditions in which you operate are very, very restrictive. If you want to go inside a building, for example, for search and rescue or to locate an active shooter, or you want to navigate under the canopy in an orchard to look at health of plants or to look for to count fruits to measure the tree trunks, these are things we do, by the way.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“And so the question you want to ask is if there are no pilots, there's no communications with any base station. There's no knowledge of position. And if there's no a priori map, a priori knowledge of what the environment looks like, a priori model of what might happen in the future, can robots navigate. So that is true autonomy.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“Third form of infrastructure we use, and I hate to call it infrastructure, but it is that in the sense of robots, it's people. So you could rely on somebody to pilot you.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“And the question Try to ask is can we make robots that will be able to make decisions? Without any kind of external infrastructure. So, what does that mean? So, the most common piece of infrastructure that airplanes use today is GPS. GPS is also the most brittle form of information. If you have driven in a city, try to use GPS navigation in tall buildings, you immediately lose GPS. And so that's not a very sophisticated way of building autonomy. I think the second piece of infrastructure they rely on is communications. It's very easy to jam communications. In fact, if you use Wi Fi, you know that Wi Fi signals drop out, cell signals drop out. So to rely on something like that is not good.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source
“Usually their wings, but there's nothing preventing us from doing this for helicopters as well. There are many military organizations that have autonomous helicopters in the same vein. And by the way, you look at autopilots and airplanes and it's actually very similar. In fact, one interesting question we can ask is if you look at all the Air safety violations, all the crashes that occurred. Would they have happened if the plane were truly autonomous? And I think you'll find that in many of the cases, because of pilot error, we make silly decisions. And so in some sense, even in air traffic, commercial air traffic, there's a lot of applications, although we only see autonomy being enabled at very high altitudes when the pilot, the plane is an autopilot.”
2019-09-08 · Lex Fridman Podcast · Vijay Kumar: Flying Robots · IDENTIFIED FROM THE TRANSCRIPT · source