WEBVTT 00:00.000 --> 00:03.080 The following is a conversation with Vijay Kumar. 00:03.080 --> 00:05.760 He's one of the top roboticists in the world, 00:05.760 --> 00:08.760 a professor at the University of Pennsylvania, 00:08.760 --> 00:12.880 a dean of pen engineering, former director of Grasp Lab, 00:12.880 --> 00:15.300 or the General Robotics Automation Sensing 00:15.300 --> 00:17.560 and Perception Laboratory at Penn, 00:17.560 --> 00:22.560 that was established back in 1979, that's 40 years ago. 00:22.600 --> 00:25.280 Vijay is perhaps best known for his work 00:25.280 --> 00:28.520 in multi robot systems, robot swarms, 00:28.520 --> 00:30.880 and micro aerial vehicles, 00:30.880 --> 00:34.020 robots that elegantly cooperate in flight 00:34.020 --> 00:36.200 under all the uncertainty and challenges 00:36.200 --> 00:38.760 that the real world conditions present. 00:38.760 --> 00:41.920 This is the Artificial Intelligence Podcast. 00:41.920 --> 00:44.320 If you enjoy it, subscribe on YouTube, 00:44.320 --> 00:47.560 give it five stars on iTunes, support on Patreon, 00:47.560 --> 00:49.500 or simply connect with me on Twitter 00:49.500 --> 00:53.280 at Lex Friedman, spelled F R I D M A N. 00:53.280 --> 00:58.280 And now, here's my conversation with Vijay Kumar. 00:58.700 --> 01:01.080 What is the first robot you've ever built 01:01.080 --> 01:02.840 or were a part of building? 01:02.840 --> 01:04.760 Way back when I was in graduate school, 01:04.760 --> 01:06.760 I was part of a fairly big project 01:06.760 --> 01:11.760 that involved building a very large hexapod. 01:12.040 --> 01:16.700 It's weighed close to 7,000 pounds, 01:17.520 --> 01:21.620 and it was powered by hydraulic actuation, 01:21.620 --> 01:26.620 or it was actuated by hydraulics with 18 motors, 01:27.720 --> 01:32.720 hydraulic motors, each controlled by an Intel 8085 processor 01:34.160 --> 01:36.680 and an 8086 co processor. 01:38.120 --> 01:43.120 And so imagine this huge monster that had 18 joints, 01:44.800 --> 01:46.960 each controlled by an independent computer, 01:46.960 --> 01:49.320 and there was a 19th computer that actually did 01:49.320 --> 01:52.320 the coordination between these 18 joints. 01:52.320 --> 01:53.720 So I was part of this project, 01:53.720 --> 01:58.720 and my thesis work was how do you coordinate the 18 legs? 02:02.080 --> 02:06.320 And in particular, the pressures in the hydraulic cylinders 02:06.320 --> 02:09.200 to get efficient locomotion. 02:09.200 --> 02:11.640 It sounds like a giant mess. 02:11.640 --> 02:14.440 So how difficult is it to make all the motors communicate? 02:14.440 --> 02:17.600 Presumably, you have to send signals hundreds of times 02:17.600 --> 02:18.440 a second, or at least. 02:18.440 --> 02:19.880 So this was not my work, 02:19.880 --> 02:23.960 but the folks who worked on this wrote what I believe 02:23.960 --> 02:26.640 to be the first multiprocessor operating system. 02:26.640 --> 02:30.320 This was in the 80s, and you had to make sure 02:30.320 --> 02:32.800 that obviously messages got across 02:32.800 --> 02:34.640 from one joint to another. 02:34.640 --> 02:37.960 You have to remember the clock speeds on those computers 02:37.960 --> 02:39.660 were about half a megahertz. 02:39.660 --> 02:42.180 Right, the 80s. 02:42.180 --> 02:45.320 So not to romanticize the notion, 02:45.320 --> 02:49.700 but how did it make you feel to see that robot move? 02:51.080 --> 02:52.280 It was amazing. 02:52.280 --> 02:55.280 In hindsight, it looks like, well, we built this thing 02:55.280 --> 02:57.320 which really should have been much smaller. 02:57.320 --> 02:59.160 And of course, today's robots are much smaller. 02:59.160 --> 03:03.120 You look at Boston Dynamics or Ghost Robotics, 03:03.120 --> 03:04.780 a spinoff from Penn. 03:06.080 --> 03:10.080 But back then, you were stuck with the substrate you had, 03:10.080 --> 03:13.720 the compute you had, so things were unnecessarily big. 03:13.720 --> 03:18.040 But at the same time, and this is just human psychology, 03:18.040 --> 03:20.400 somehow bigger means grander. 03:21.600 --> 03:23.640 People never had the same appreciation 03:23.640 --> 03:26.360 for nanotechnology or nanodevices 03:26.360 --> 03:30.160 as they do for the Space Shuttle or the Boeing 747. 03:30.160 --> 03:32.760 Yeah, you've actually done quite a good job 03:32.760 --> 03:36.000 at illustrating that small is beautiful 03:36.000 --> 03:37.760 in terms of robotics. 03:37.760 --> 03:42.600 So what is on that topic is the most beautiful 03:42.600 --> 03:46.200 or elegant robot in motion that you've ever seen? 03:46.200 --> 03:47.880 Not to pick favorites or whatever, 03:47.880 --> 03:51.000 but something that just inspires you that you remember. 03:51.000 --> 03:54.000 Well, I think the thing that I'm most proud of 03:54.000 --> 03:57.200 that my students have done is really think about 03:57.200 --> 04:00.360 small UAVs that can maneuver in constrained spaces 04:00.360 --> 04:03.640 and in particular, their ability to coordinate 04:03.640 --> 04:06.760 with each other and form three dimensional patterns. 04:06.760 --> 04:08.920 So once you can do that, 04:08.920 --> 04:13.920 you can essentially create 3D objects in the sky 04:14.960 --> 04:17.680 and you can deform these objects on the fly. 04:17.680 --> 04:21.560 So in some sense, your toolbox of what you can create 04:21.560 --> 04:23.400 has suddenly got enhanced. 04:25.240 --> 04:27.800 And before that, we did the two dimensional version of this. 04:27.800 --> 04:31.680 So we had ground robots forming patterns and so on. 04:31.680 --> 04:34.960 So that was not as impressive, that was not as beautiful. 04:34.960 --> 04:36.560 But if you do it in 3D, 04:36.560 --> 04:40.240 suspended in midair, and you've got to go back to 2011 04:40.240 --> 04:43.040 when we did this, now it's actually pretty standard 04:43.040 --> 04:45.600 to do these things eight years later. 04:45.600 --> 04:47.680 But back then it was a big accomplishment. 04:47.680 --> 04:50.280 So the distributed cooperation 04:50.280 --> 04:53.480 is where beauty emerges in your eyes? 04:53.480 --> 04:55.800 Well, I think beauty to an engineer is very different 04:55.800 --> 04:59.400 from beauty to someone who's looking at robots 04:59.400 --> 05:01.240 from the outside, if you will. 05:01.240 --> 05:04.800 But what I meant there, so before we said that grand, 05:04.800 --> 05:09.800 so before we said that grand is associated with size. 05:10.520 --> 05:13.720 And another way of thinking about this 05:13.720 --> 05:15.600 is just the physical shape 05:15.600 --> 05:18.400 and the idea that you can get physical shapes in midair 05:18.400 --> 05:21.560 and have them deform, that's beautiful. 05:21.560 --> 05:23.040 But the individual components, 05:23.040 --> 05:24.880 the agility is beautiful too, right? 05:24.880 --> 05:25.720 That is true too. 05:25.720 --> 05:28.480 So then how quickly can you actually manipulate 05:28.480 --> 05:29.560 these three dimensional shapes 05:29.560 --> 05:31.280 and the individual components? 05:31.280 --> 05:32.240 Yes, you're right. 05:32.240 --> 05:36.760 But by the way, you said UAV, unmanned aerial vehicle. 05:36.760 --> 05:41.760 What's a good term for drones, UAVs, quad copters? 05:41.840 --> 05:44.560 Is there a term that's being standardized? 05:44.560 --> 05:45.440 I don't know if there is. 05:45.440 --> 05:47.920 Everybody wants to use the word drones. 05:47.920 --> 05:51.080 And I've often said this, drones to me is a pejorative word. 05:51.080 --> 05:53.960 It signifies something that's dumb, 05:53.960 --> 05:56.360 that's pre programmed, that does one little thing 05:56.360 --> 05:58.600 and robots are anything but drones. 05:58.600 --> 06:00.680 So I actually don't like that word, 06:00.680 --> 06:02.960 but that's what everybody uses. 06:02.960 --> 06:04.880 You could call it unpiloted. 06:04.880 --> 06:05.800 Unpiloted. 06:05.800 --> 06:08.120 But even unpiloted could be radio controlled, 06:08.120 --> 06:11.560 could be remotely controlled in many different ways. 06:11.560 --> 06:12.960 And I think the right word is, 06:12.960 --> 06:15.040 thinking about it as an aerial robot. 06:15.040 --> 06:19.080 You also say agile, autonomous, aerial robot, right? 06:19.080 --> 06:22.160 Yeah, so agility is an attribute, but they don't have to be. 06:23.080 --> 06:24.800 So what biological system, 06:24.800 --> 06:27.200 because you've also drawn a lot of inspiration with those. 06:27.200 --> 06:30.360 I've seen bees and ants that you've talked about. 06:30.360 --> 06:35.240 What living creatures have you found to be most inspiring 06:35.240 --> 06:38.520 as an engineer, instructive in your work in robotics? 06:38.520 --> 06:43.440 To me, so ants are really quite incredible creatures, right? 06:43.440 --> 06:47.880 So you, I mean, the individuals arguably are very simple 06:47.880 --> 06:52.360 in how they're built and yet they're incredibly resilient 06:52.360 --> 06:53.960 as a population. 06:53.960 --> 06:56.760 And as individuals, they're incredibly robust. 06:56.760 --> 07:00.600 So, if you take an ant, it's six legs, 07:00.600 --> 07:04.120 you remove one leg, it still works just fine. 07:04.120 --> 07:05.760 And it moves along. 07:05.760 --> 07:08.720 And I don't know that he even realizes it's lost a leg. 07:09.760 --> 07:12.520 So that's the robustness at the individual ant level. 07:13.400 --> 07:15.360 But then you look about this instinct 07:15.360 --> 07:17.680 for self preservation of the colonies 07:17.680 --> 07:20.400 and they adapt in so many amazing ways. 07:20.400 --> 07:25.400 You know, transcending gaps by just chaining themselves 07:26.800 --> 07:29.600 together when you have a flood, 07:29.600 --> 07:32.360 being able to recruit other teammates 07:32.360 --> 07:34.320 to carry big morsels of food, 07:35.760 --> 07:38.760 and then going out in different directions looking for food, 07:38.760 --> 07:43.160 and then being able to demonstrate consensus, 07:43.160 --> 07:47.040 even though they don't communicate directly with each other 07:47.040 --> 07:49.080 the way we communicate with each other. 07:49.080 --> 07:51.880 In some sense, they also know how to do democracy, 07:51.880 --> 07:53.640 probably better than what we do. 07:53.640 --> 07:57.000 Yeah, somehow it's even democracy is emergent. 07:57.000 --> 07:59.120 It seems like all of the phenomena that we see 07:59.120 --> 08:00.480 is all emergent. 08:00.480 --> 08:03.560 It seems like there's no centralized communicator. 08:03.560 --> 08:06.520 There is, so I think a lot is made about that word, 08:06.520 --> 08:09.640 emergent, and it means lots of things to different people. 08:09.640 --> 08:10.680 But you're absolutely right. 08:10.680 --> 08:13.040 I think as an engineer, you think about 08:13.040 --> 08:17.720 what element, elemental behaviors 08:17.720 --> 08:21.320 were primitives you could synthesize 08:21.320 --> 08:25.240 so that the whole looks incredibly powerful, 08:25.240 --> 08:26.520 incredibly synergistic, 08:26.520 --> 08:29.520 the whole definitely being greater than some of the parts, 08:29.520 --> 08:31.480 and ants are living proof of that. 08:32.480 --> 08:34.960 So when you see these beautiful swarms 08:34.960 --> 08:37.520 where there's biological systems of robots, 08:38.520 --> 08:40.200 do you sometimes think of them 08:40.200 --> 08:44.640 as a single individual living intelligent organism? 08:44.640 --> 08:47.400 So it's the same as thinking of our human beings 08:47.400 --> 08:51.160 are human civilization as one organism, 08:51.160 --> 08:52.960 or do you still, as an engineer, 08:52.960 --> 08:54.600 think about the individual components 08:54.600 --> 08:55.440 and all the engineering 08:55.440 --> 08:57.320 that went into the individual components? 08:57.320 --> 08:58.640 Well, that's very interesting. 08:58.640 --> 09:01.480 So again, philosophically as engineers, 09:01.480 --> 09:05.400 what we wanna do is to go beyond 09:05.400 --> 09:08.280 the individual components, the individual units, 09:08.280 --> 09:11.520 and think about it as a unit, as a cohesive unit, 09:11.520 --> 09:15.120 without worrying about the individual components. 09:15.120 --> 09:17.760 If you start obsessing about 09:17.760 --> 09:22.120 the individual building blocks and what they do, 09:23.320 --> 09:27.960 you inevitably will find it hard to scale up. 09:27.960 --> 09:29.000 Just mathematically, 09:29.000 --> 09:31.600 just think about individual things you wanna model, 09:31.600 --> 09:34.040 and if you want to have 10 of those, 09:34.040 --> 09:36.440 then you essentially are taking Cartesian products 09:36.440 --> 09:39.320 of 10 things, and that makes it really complicated. 09:39.320 --> 09:41.840 Then to do any kind of synthesis or design 09:41.840 --> 09:44.200 in that high dimension space is really hard. 09:44.200 --> 09:45.800 So the right way to do this 09:45.800 --> 09:49.040 is to think about the individuals in a clever way 09:49.040 --> 09:51.120 so that at the higher level, 09:51.120 --> 09:53.400 when you look at lots and lots of them, 09:53.400 --> 09:55.320 abstractly, you can think of them 09:55.320 --> 09:57.120 in some low dimensional space. 09:57.120 --> 09:58.680 So what does that involve? 09:58.680 --> 10:02.160 For the individual, do you have to try to make 10:02.160 --> 10:05.160 the way they see the world as local as possible? 10:05.160 --> 10:06.440 And the other thing, 10:06.440 --> 10:09.560 do you just have to make them robust to collisions? 10:09.560 --> 10:10.880 Like you said with the ants, 10:10.880 --> 10:15.320 if something fails, the whole swarm doesn't fail. 10:15.320 --> 10:17.760 Right, I think as engineers, we do this. 10:17.760 --> 10:19.760 I mean, you think about, we build planes, 10:19.760 --> 10:21.240 or we build iPhones, 10:22.240 --> 10:26.280 and we know that by taking individual components, 10:26.280 --> 10:30.080 well engineered components with well specified interfaces 10:30.080 --> 10:31.680 that behave in a predictable way, 10:31.680 --> 10:33.560 you can build complex systems. 10:34.440 --> 10:36.880 So that's ingrained, I would claim, 10:36.880 --> 10:39.400 in most engineers thinking, 10:39.400 --> 10:41.600 and it's true for computer scientists as well. 10:41.600 --> 10:44.760 I think what's different here is that you want 10:44.760 --> 10:49.480 the individuals to be robust in some sense, 10:49.480 --> 10:52.000 as we do in these other settings, 10:52.000 --> 10:54.480 but you also want some degree of resiliency 10:54.480 --> 10:56.320 for the population. 10:56.320 --> 11:00.560 And so you really want them to be able to reestablish 11:02.040 --> 11:03.840 communication with their neighbors. 11:03.840 --> 11:08.840 You want them to rethink their strategy for group behavior. 11:08.840 --> 11:10.760 You want them to reorganize. 11:12.200 --> 11:15.920 And that's where I think a lot of the challenges lie. 11:15.920 --> 11:18.160 So just at a high level, 11:18.160 --> 11:20.880 what does it take for a bunch of, 11:22.200 --> 11:24.440 what should we call them, flying robots, 11:24.440 --> 11:26.680 to create a formation? 11:26.680 --> 11:28.680 Just for people who are not familiar 11:28.680 --> 11:32.760 with robotics in general, how much information is needed? 11:32.760 --> 11:35.840 How do you even make it happen 11:35.840 --> 11:39.520 without a centralized controller? 11:39.520 --> 11:41.080 So, I mean, there are a couple of different ways 11:41.080 --> 11:43.160 of looking at this. 11:43.160 --> 11:45.680 If you are a purist, 11:45.680 --> 11:50.680 you think of it as a way of recreating what nature does. 11:53.560 --> 11:58.440 So nature forms groups for several reasons, 11:58.440 --> 12:02.000 but mostly it's because of this instinct 12:02.000 --> 12:05.680 that organisms have of preserving their colonies, 12:05.680 --> 12:09.520 their population, which means what? 12:09.520 --> 12:12.920 You need shelter, you need food, you need to procreate, 12:12.920 --> 12:14.760 and that's basically it. 12:14.760 --> 12:18.440 So the kinds of interactions you see are all organic. 12:18.440 --> 12:19.760 They're all local. 12:20.760 --> 12:24.080 And the only information that they share, 12:24.080 --> 12:27.520 and mostly it's indirectly, is to, again, 12:27.520 --> 12:30.000 preserve the herd or the flock, 12:30.000 --> 12:35.000 or the swarm, and either by looking for new sources of food 12:37.480 --> 12:39.440 or looking for new shelters, right? 12:39.440 --> 12:40.280 Right. 12:41.240 --> 12:45.360 As engineers, when we build swarms, we have a mission. 12:46.560 --> 12:51.560 And when you think of a mission, and it involves mobility, 12:52.480 --> 12:55.000 most often it's described in some kind 12:55.000 --> 12:56.880 of a global coordinate system. 12:56.880 --> 12:59.440 As a human, as an operator, as a commander, 12:59.440 --> 13:03.560 or as a collaborator, I have my coordinate system, 13:03.560 --> 13:06.640 and I want the robots to be consistent with that. 13:07.600 --> 13:11.240 So I might think of it slightly differently. 13:11.240 --> 13:15.440 I might want the robots to recognize that coordinate system, 13:15.440 --> 13:17.720 which means not only do they have to think locally 13:17.720 --> 13:19.600 in terms of who their immediate neighbors are, 13:19.600 --> 13:20.920 but they have to be cognizant 13:20.920 --> 13:24.040 of what the global environment is. 13:24.040 --> 13:27.040 They have to be cognizant of what the global environment 13:27.040 --> 13:28.280 looks like. 13:28.280 --> 13:31.040 So if I say, surround this building 13:31.040 --> 13:33.240 and protect this from intruders, 13:33.240 --> 13:35.600 well, they're immediately in a building centered 13:35.600 --> 13:37.040 coordinate system, and I have to tell them 13:37.040 --> 13:38.680 where the building is. 13:38.680 --> 13:40.040 And they're globally collaborating 13:40.040 --> 13:41.280 on the map of that building. 13:41.280 --> 13:44.160 They're maintaining some kind of global, 13:44.160 --> 13:45.480 not just in the frame of the building, 13:45.480 --> 13:49.000 but there's information that's ultimately being built up 13:49.000 --> 13:53.280 explicitly as opposed to kind of implicitly, 13:53.280 --> 13:54.360 like nature might. 13:54.360 --> 13:55.200 Correct, correct. 13:55.200 --> 13:57.680 So in some sense, nature is very, very sophisticated, 13:57.680 --> 14:01.880 but the tasks that nature solves or needs to solve 14:01.880 --> 14:05.160 are very different from the kind of engineered tasks, 14:05.160 --> 14:09.760 artificial tasks that we are forced to address. 14:09.760 --> 14:12.520 And again, there's nothing preventing us 14:12.520 --> 14:15.160 from solving these other problems, 14:15.160 --> 14:16.600 but ultimately it's about impact. 14:16.600 --> 14:19.360 You want these swarms to do something useful. 14:19.360 --> 14:24.360 And so you're kind of driven into this very unnatural, 14:24.640 --> 14:25.480 if you will. 14:25.480 --> 14:29.160 Unnatural, meaning not like how nature does, setting. 14:29.160 --> 14:31.920 And it's probably a little bit more expensive 14:31.920 --> 14:33.760 to do it the way nature does, 14:33.760 --> 14:37.560 because nature is less sensitive 14:37.560 --> 14:39.480 to the loss of the individual. 14:39.480 --> 14:42.280 And cost wise in robotics, 14:42.280 --> 14:45.480 I think you're more sensitive to losing individuals. 14:45.480 --> 14:49.000 I think that's true, although if you look at the price 14:49.000 --> 14:51.520 to performance ratio of robotic components, 14:51.520 --> 14:54.720 it's coming down dramatically, right? 14:54.720 --> 14:56.040 It continues to come down. 14:56.040 --> 14:58.920 So I think we're asymptotically approaching the point 14:58.920 --> 14:59.960 where we would get, yeah, 14:59.960 --> 15:05.040 the cost of individuals would really become insignificant. 15:05.040 --> 15:07.640 So let's step back at a high level view, 15:07.640 --> 15:12.480 the impossible question of what kind of, as an overview, 15:12.480 --> 15:14.400 what kind of autonomous flying vehicles 15:14.400 --> 15:16.200 are there in general? 15:16.200 --> 15:19.720 I think the ones that receive a lot of notoriety 15:19.720 --> 15:22.560 are obviously the military vehicles. 15:22.560 --> 15:26.280 Military vehicles are controlled by a base station, 15:26.280 --> 15:29.640 but have a lot of human supervision. 15:29.640 --> 15:31.800 But they have limited autonomy, 15:31.800 --> 15:34.760 which is the ability to go from point A to point B. 15:34.760 --> 15:37.080 And even the more sophisticated now, 15:37.080 --> 15:40.400 sophisticated vehicles can do autonomous takeoff 15:40.400 --> 15:41.760 and landing. 15:41.760 --> 15:44.360 And those usually have wings and they're heavy. 15:44.360 --> 15:45.360 Usually they're wings, 15:45.360 --> 15:47.440 but then there's nothing preventing us from doing this 15:47.440 --> 15:49.000 for helicopters as well. 15:49.000 --> 15:52.480 There are many military organizations 15:52.480 --> 15:56.560 that have autonomous helicopters in the same vein. 15:56.560 --> 16:00.080 And by the way, you look at autopilots and airplanes 16:00.080 --> 16:02.840 and it's actually very similar. 16:02.840 --> 16:07.160 In fact, one interesting question we can ask is, 16:07.160 --> 16:12.120 if you look at all the air safety violations, 16:12.120 --> 16:14.080 all the crashes that occurred, 16:14.080 --> 16:18.640 would they have happened if the plane were truly autonomous? 16:18.640 --> 16:21.960 And I think you'll find that in many of the cases, 16:21.960 --> 16:24.600 because of pilot error, we made silly decisions. 16:24.600 --> 16:26.960 And so in some sense, even in air traffic, 16:26.960 --> 16:29.800 commercial air traffic, there's a lot of applications, 16:29.800 --> 16:33.960 although we only see autonomy being enabled 16:33.960 --> 16:38.960 at very high altitudes when the plane is an autopilot. 16:38.960 --> 16:41.960 The plane is an autopilot. 16:41.960 --> 16:42.800 There's still a role for the human 16:42.800 --> 16:47.640 and that kind of autonomy is, you're kind of implying, 16:47.640 --> 16:48.680 I don't know what the right word is, 16:48.680 --> 16:53.480 but it's a little dumber than it could be. 16:53.480 --> 16:55.720 Right, so in the lab, of course, 16:55.720 --> 16:59.200 we can afford to be a lot more aggressive. 16:59.200 --> 17:04.200 And the question we try to ask is, 17:04.200 --> 17:09.200 can we make robots that will be able to make decisions 17:10.360 --> 17:13.680 without any kind of external infrastructure? 17:13.680 --> 17:14.880 So what does that mean? 17:14.880 --> 17:16.960 So the most common piece of infrastructure 17:16.960 --> 17:19.640 that airplanes use today is GPS. 17:20.560 --> 17:25.160 GPS is also the most brittle form of information. 17:26.680 --> 17:30.480 If you have driven in a city, try to use GPS navigation, 17:30.480 --> 17:32.760 in tall buildings, you immediately lose GPS. 17:32.760 --> 17:36.280 And so that's not a very sophisticated way 17:36.280 --> 17:37.840 of building autonomy. 17:37.840 --> 17:39.560 I think the second piece of infrastructure 17:39.560 --> 17:41.920 they rely on is communications. 17:41.920 --> 17:46.200 Again, it's very easy to jam communications. 17:47.360 --> 17:51.320 In fact, if you use wifi, you know that wifi signals 17:51.320 --> 17:53.520 drop out, cell signals drop out. 17:53.520 --> 17:56.800 So to rely on something like that is not good. 17:58.560 --> 18:01.200 The third form of infrastructure we use, 18:01.200 --> 18:02.920 and I hate to call it infrastructure, 18:02.920 --> 18:06.360 but it is that, in the sense of robots, is people. 18:06.360 --> 18:08.640 So you could rely on somebody to pilot you. 18:09.960 --> 18:11.600 And so the question you wanna ask is, 18:11.600 --> 18:14.760 if there are no pilots, there's no communications 18:14.760 --> 18:18.720 with any base station, if there's no knowledge of position, 18:18.720 --> 18:21.640 and if there's no a priori map, 18:21.640 --> 18:24.880 a priori knowledge of what the environment looks like, 18:24.880 --> 18:28.240 a priori model of what might happen in the future, 18:28.240 --> 18:29.560 can robots navigate? 18:29.560 --> 18:31.480 So that is true autonomy. 18:31.480 --> 18:34.160 So that's true autonomy, and we're talking about, 18:34.160 --> 18:36.880 you mentioned like military application of drones. 18:36.880 --> 18:38.320 Okay, so what else is there? 18:38.320 --> 18:42.080 You talk about agile, autonomous flying robots, 18:42.080 --> 18:45.680 aerial robots, so that's a different kind of, 18:45.680 --> 18:48.160 it's not winged, it's not big, at least it's small. 18:48.160 --> 18:50.840 So I use the word agility mostly, 18:50.840 --> 18:53.520 or at least we're motivated to do agile robots, 18:53.520 --> 18:58.000 mostly because robots can operate 18:58.000 --> 19:01.120 and should be operating in constrained environments. 19:02.120 --> 19:06.960 And if you want to operate the way a global hawk operates, 19:06.960 --> 19:09.120 I mean, the kinds of conditions in which you operate 19:09.120 --> 19:10.760 are very, very restrictive. 19:11.760 --> 19:13.720 If you wanna go inside a building, 19:13.720 --> 19:15.600 for example, for search and rescue, 19:15.600 --> 19:18.120 or to locate an active shooter, 19:18.120 --> 19:22.120 or you wanna navigate under the canopy in an orchard 19:22.120 --> 19:23.880 to look at health of plants, 19:23.880 --> 19:28.240 or to look for, to count fruits, 19:28.240 --> 19:31.240 to measure the tree trunks. 19:31.240 --> 19:33.240 These are things we do, by the way. 19:33.240 --> 19:35.400 There's some cool agriculture stuff you've shown 19:35.400 --> 19:37.080 in the past, it's really awesome. 19:37.080 --> 19:40.360 So in those kinds of settings, you do need that agility. 19:40.360 --> 19:42.560 Agility does not necessarily mean 19:42.560 --> 19:45.440 you break records for the 100 meters dash. 19:45.440 --> 19:48.000 What it really means is you see the unexpected 19:48.000 --> 19:51.480 and you're able to maneuver in a safe way, 19:51.480 --> 19:55.400 and in a way that gets you the most information 19:55.400 --> 19:57.640 about the thing you're trying to do. 19:57.640 --> 20:00.440 By the way, you may be the only person 20:00.440 --> 20:04.200 who, in a TED Talk, has used a math equation, 20:04.200 --> 20:07.600 which is amazing, people should go see one of your TED Talks. 20:07.600 --> 20:08.800 Actually, it's very interesting, 20:08.800 --> 20:12.400 because the TED curator, Chris Anderson, 20:12.400 --> 20:15.360 told me, you can't show math. 20:15.360 --> 20:18.200 And I thought about it, but that's who I am. 20:18.200 --> 20:20.760 I mean, that's our work. 20:20.760 --> 20:25.760 And so I felt compelled to give the audience a taste 20:25.760 --> 20:27.640 for at least some math. 20:27.640 --> 20:32.640 So on that point, simply, what does it take 20:32.880 --> 20:37.360 to make a thing with four motors fly, a quadcopter, 20:37.360 --> 20:40.640 one of these little flying robots? 20:41.760 --> 20:43.960 How hard is it to make it fly? 20:43.960 --> 20:46.560 How do you coordinate the four motors? 20:46.560 --> 20:51.560 How do you convert those motors into actual movement? 20:52.600 --> 20:54.800 So this is an interesting question. 20:54.800 --> 20:58.080 We've been trying to do this since 2000. 20:58.080 --> 21:00.560 It is a commentary on the sensors 21:00.560 --> 21:02.080 that were available back then, 21:02.080 --> 21:04.280 the computers that were available back then. 21:05.560 --> 21:10.280 And a number of things happened between 2000 and 2007. 21:11.520 --> 21:14.120 One is the advances in computing, 21:14.120 --> 21:16.760 which is, so we all know about Moore's Law, 21:16.760 --> 21:19.680 but I think 2007 was a tipping point, 21:19.680 --> 21:22.720 the year of the iPhone, the year of the cloud. 21:22.720 --> 21:24.640 Lots of things happened in 2007. 21:25.600 --> 21:27.600 But going back even further, 21:27.600 --> 21:31.360 inertial measurement units as a sensor really matured. 21:31.360 --> 21:33.040 Again, lots of reasons for that. 21:33.920 --> 21:35.400 Certainly, there's a lot of federal funding, 21:35.400 --> 21:37.360 particularly DARPA in the US, 21:38.320 --> 21:42.760 but they didn't anticipate this boom in IMUs. 21:42.760 --> 21:46.560 But if you look, subsequently what happened 21:46.560 --> 21:50.040 is that every car manufacturer had to put an airbag in, 21:50.040 --> 21:52.600 which meant you had to have an accelerometer on board. 21:52.600 --> 21:55.000 And so that drove down the price to performance ratio. 21:55.000 --> 21:56.880 Wow, I should know this. 21:56.880 --> 21:57.960 That's very interesting. 21:57.960 --> 21:59.360 That's very interesting, the connection there. 21:59.360 --> 22:01.320 And that's why research is very, 22:01.320 --> 22:03.280 it's very hard to predict the outcomes. 22:04.840 --> 22:07.640 And again, the federal government spent a ton of money 22:07.640 --> 22:12.280 on things that they thought were useful for resonators, 22:12.280 --> 22:16.840 but it ended up enabling these small UAVs, which is great, 22:16.840 --> 22:18.520 because I could have never raised that much money 22:18.520 --> 22:20.760 and sold this project, 22:20.760 --> 22:22.200 hey, we want to build these small UAVs. 22:22.200 --> 22:25.440 Can you actually fund the development of low cost IMUs? 22:25.440 --> 22:27.600 So why do you need an IMU on an IMU? 22:27.600 --> 22:31.000 So I'll come back to that. 22:31.000 --> 22:33.320 So in 2007, 2008, we were able to build these. 22:33.320 --> 22:35.200 And then the question you're asking was a good one. 22:35.200 --> 22:40.240 How do you coordinate the motors to develop this? 22:40.240 --> 22:43.880 But over the last 10 years, everything is commoditized. 22:43.880 --> 22:46.240 A high school kid today can pick up 22:46.240 --> 22:50.560 a Raspberry Pi kit and build this. 22:50.560 --> 22:53.200 All the low levels functionality is all automated. 22:54.160 --> 22:56.360 But basically at some level, 22:56.360 --> 23:01.360 you have to drive the motors at the right RPMs, 23:01.360 --> 23:03.680 the right velocity, 23:04.560 --> 23:07.480 in order to generate the right amount of thrust, 23:07.480 --> 23:10.360 in order to position it and orient it in a way 23:10.360 --> 23:12.840 that you need to in order to fly. 23:13.800 --> 23:16.680 The feedback that you get is from onboard sensors, 23:16.680 --> 23:18.400 and the IMU is an important part of it. 23:18.400 --> 23:23.400 The IMU tells you what the acceleration is, 23:23.840 --> 23:26.400 as well as what the angular velocity is. 23:26.400 --> 23:29.200 And those are important pieces of information. 23:30.440 --> 23:34.200 In addition to that, you need some kind of local position 23:34.200 --> 23:37.480 or velocity information. 23:37.480 --> 23:39.360 For example, when we walk, 23:39.360 --> 23:41.560 we implicitly have this information 23:41.560 --> 23:45.840 because we kind of know what our stride length is. 23:46.720 --> 23:51.480 We also are looking at images fly past our retina, 23:51.480 --> 23:54.280 if you will, and so we can estimate velocity. 23:54.280 --> 23:56.360 We also have accelerometers in our head, 23:56.360 --> 23:59.160 and we're able to integrate all these pieces of information 23:59.160 --> 24:02.360 to determine where we are as we walk. 24:02.360 --> 24:04.320 And so robots have to do something very similar. 24:04.320 --> 24:08.160 You need an IMU, you need some kind of a camera 24:08.160 --> 24:11.640 or other sensor that's measuring velocity, 24:12.560 --> 24:15.800 and then you need some kind of a global reference frame 24:15.800 --> 24:19.520 if you really want to think about doing something 24:19.520 --> 24:21.280 in a world coordinate system. 24:21.280 --> 24:23.680 And so how do you estimate your position 24:23.680 --> 24:25.160 with respect to that global reference frame? 24:25.160 --> 24:26.560 That's important as well. 24:26.560 --> 24:29.520 So coordinating the RPMs of the four motors 24:29.520 --> 24:32.640 is what allows you to, first of all, fly and hover, 24:32.640 --> 24:35.600 and then you can change the orientation 24:35.600 --> 24:37.600 and the velocity and so on. 24:37.600 --> 24:38.440 Exactly, exactly. 24:38.440 --> 24:40.320 So it's a bunch of degrees of freedom 24:40.320 --> 24:41.160 that you're complaining about. 24:41.160 --> 24:42.200 There's six degrees of freedom, 24:42.200 --> 24:44.920 but you only have four inputs, the four motors. 24:44.920 --> 24:49.920 And it turns out to be a remarkably versatile configuration. 24:50.920 --> 24:53.080 You think at first, well, I only have four motors, 24:53.080 --> 24:55.000 how do I go sideways? 24:55.000 --> 24:57.280 But it's not too hard to say, well, if I tilt myself, 24:57.280 --> 25:00.440 I can go sideways, and then you have four motors 25:00.440 --> 25:03.320 pointing up, how do I rotate in place 25:03.320 --> 25:05.360 about a vertical axis? 25:05.360 --> 25:07.800 Well, you rotate them at different speeds 25:07.800 --> 25:09.720 and that generates reaction moments 25:09.720 --> 25:11.520 and that allows you to turn. 25:11.520 --> 25:14.960 So it's actually a pretty, it's an optimal configuration 25:14.960 --> 25:17.040 from an engineer standpoint. 25:18.360 --> 25:23.360 It's very simple, very cleverly done, and very versatile. 25:23.360 --> 25:27.240 So if you could step back to a time, 25:27.240 --> 25:30.000 so I've always known flying robots as, 25:31.040 --> 25:35.760 to me, it was natural that a quadcopter should fly. 25:35.760 --> 25:37.880 But when you first started working with it, 25:38.800 --> 25:42.000 how surprised are you that you can make, 25:42.000 --> 25:45.520 do so much with the four motors? 25:45.520 --> 25:47.600 How surprising is it that you can make this thing fly, 25:47.600 --> 25:49.760 first of all, that you can make it hover, 25:49.760 --> 25:52.000 that you can add control to it? 25:52.000 --> 25:55.080 Firstly, this is not, the four motor configuration 25:55.080 --> 25:56.400 is not ours. 25:56.400 --> 25:59.320 You can, it has at least a hundred year history. 26:00.320 --> 26:04.160 And various people, various people try to get quadrotors 26:04.160 --> 26:06.840 to fly without much success. 26:08.480 --> 26:10.760 As I said, we've been working on this since 2000. 26:10.760 --> 26:14.400 Our first designs were, well, this is way too complicated. 26:14.400 --> 26:18.480 Why not we try to get an omnidirectional flying robot? 26:18.480 --> 26:21.760 So our early designs, we had eight rotors. 26:21.760 --> 26:25.200 And so these eight rotors were arranged uniformly 26:26.600 --> 26:28.000 on a sphere, if you will. 26:28.000 --> 26:30.440 So you can imagine a symmetric configuration. 26:30.440 --> 26:33.280 And so you should be able to fly anywhere. 26:33.280 --> 26:36.240 But the real challenge we had is the strength to weight ratio 26:36.240 --> 26:37.080 is not enough. 26:37.080 --> 26:39.680 And of course, we didn't have the sensors and so on. 26:40.520 --> 26:43.040 So everybody knew, or at least the people 26:43.040 --> 26:44.800 who worked with rotorcrafts knew, 26:44.800 --> 26:46.520 four rotors will get it done. 26:47.520 --> 26:49.400 So that was not our idea. 26:49.400 --> 26:52.800 But it took a while before we could actually do 26:52.800 --> 26:56.920 the onboard sensing and the computation that was needed 26:56.920 --> 27:01.000 for the kinds of agile maneuvering that we wanted to do 27:01.000 --> 27:03.000 in our little aerial robots. 27:03.000 --> 27:07.560 And that only happened between 2007 and 2009 in our lab. 27:07.560 --> 27:09.960 Yeah, and you have to send the signal 27:09.960 --> 27:12.480 maybe a hundred times a second. 27:12.480 --> 27:15.960 So the compute there, everything has to come down in price. 27:15.960 --> 27:20.960 And what are the steps of getting from point A to point B? 27:21.720 --> 27:25.200 So we just talked about like local control. 27:25.200 --> 27:30.200 But if all the kind of cool dancing in the air 27:30.840 --> 27:34.520 that I've seen you show, how do you make it happen? 27:34.520 --> 27:37.360 How do you make a trajectory? 27:37.360 --> 27:40.520 First of all, okay, figure out a trajectory. 27:40.520 --> 27:41.680 So plan a trajectory. 27:41.680 --> 27:44.400 And then how do you make that trajectory happen? 27:44.400 --> 27:47.280 Yeah, I think planning is a very fundamental problem 27:47.280 --> 27:48.120 in robotics. 27:48.120 --> 27:50.800 I think 10 years ago it was an esoteric thing, 27:50.800 --> 27:53.040 but today with self driving cars, 27:53.040 --> 27:55.840 everybody can understand this basic idea 27:55.840 --> 27:57.920 that a car sees a whole bunch of things 27:57.920 --> 28:00.320 and it has to keep a lane or maybe make a right turn 28:00.320 --> 28:01.280 or switch lanes. 28:01.280 --> 28:02.680 It has to plan a trajectory. 28:02.680 --> 28:03.560 It has to be safe. 28:03.560 --> 28:04.840 It has to be efficient. 28:04.840 --> 28:06.640 So everybody's familiar with that. 28:06.640 --> 28:10.240 That's kind of the first step that you have to think about 28:10.240 --> 28:14.800 when you say autonomy. 28:14.800 --> 28:19.120 And so for us, it's about finding smooth motions, 28:19.120 --> 28:21.320 motions that are safe. 28:21.320 --> 28:22.880 So we think about these two things. 28:22.880 --> 28:24.680 One is optimality, one is safety. 28:24.680 --> 28:27.200 Clearly you cannot compromise safety. 28:28.440 --> 28:31.360 So you're looking for safe, optimal motions. 28:31.360 --> 28:34.480 The other thing you have to think about is 28:34.480 --> 28:38.160 can you actually compute a reasonable trajectory 28:38.160 --> 28:40.760 in a small amount of time? 28:40.760 --> 28:42.280 Cause you have a time budget. 28:42.280 --> 28:45.160 So the optimal becomes suboptimal, 28:45.160 --> 28:50.160 but in our lab we focus on synthesizing smooth trajectory 28:51.160 --> 28:53.000 that satisfy all the constraints. 28:53.000 --> 28:57.120 In other words, don't violate any safety constraints 28:58.440 --> 29:02.880 and is as efficient as possible. 29:02.880 --> 29:04.360 And when I say efficient, 29:04.360 --> 29:06.600 it could mean I want to get from point A to point B 29:06.600 --> 29:08.360 as quickly as possible, 29:08.360 --> 29:11.840 or I want to get to it as gracefully as possible, 29:12.840 --> 29:15.960 or I want to consume as little energy as possible. 29:15.960 --> 29:18.240 But always staying within the safety constraints. 29:18.240 --> 29:22.800 But yes, always finding a safe trajectory. 29:22.800 --> 29:25.040 So there's a lot of excitement and progress 29:25.040 --> 29:27.360 in the field of machine learning 29:27.360 --> 29:29.360 and reinforcement learning 29:29.360 --> 29:32.200 and the neural network variant of that 29:32.200 --> 29:33.920 with deep reinforcement learning. 29:33.920 --> 29:36.360 Do you see a role of machine learning 29:36.360 --> 29:40.560 in, so a lot of the success of flying robots 29:40.560 --> 29:42.320 did not rely on machine learning, 29:42.320 --> 29:45.040 except for maybe a little bit of the perception 29:45.040 --> 29:46.600 on the computer vision side. 29:46.600 --> 29:48.440 On the control side and the planning, 29:48.440 --> 29:50.400 do you see there's a role in the future 29:50.400 --> 29:51.680 for machine learning? 29:51.680 --> 29:53.800 So let me disagree a little bit with you. 29:53.800 --> 29:56.800 I think we never perhaps called out in my work, 29:56.800 --> 29:57.720 called out learning, 29:57.720 --> 30:00.600 but even this very simple idea of being able to fly 30:00.600 --> 30:02.200 through a constrained space. 30:02.200 --> 30:05.680 The first time you try it, you'll invariably, 30:05.680 --> 30:08.440 you might get it wrong if the task is challenging. 30:08.440 --> 30:12.200 And the reason is to get it perfectly right, 30:12.200 --> 30:14.600 you have to model everything in the environment. 30:15.600 --> 30:19.960 And flying is notoriously hard to model. 30:19.960 --> 30:24.960 There are aerodynamic effects that we constantly discover. 30:26.520 --> 30:29.440 Even just before I was talking to you, 30:29.440 --> 30:33.440 I was talking to a student about how blades flap 30:33.440 --> 30:35.320 when they fly. 30:35.320 --> 30:40.320 And that ends up changing how a rotorcraft 30:40.880 --> 30:43.960 is accelerated in the angular direction. 30:43.960 --> 30:46.360 Does he use like micro flaps or something? 30:46.360 --> 30:47.280 It's not micro flaps. 30:47.280 --> 30:49.640 So we assume that each blade is rigid, 30:49.640 --> 30:51.720 but actually it flaps a little bit. 30:51.720 --> 30:52.880 It bends. 30:52.880 --> 30:53.720 Interesting, yeah. 30:53.720 --> 30:56.040 And so the models rely on the fact, 30:56.040 --> 30:58.640 on the assumption that they're not rigid. 30:58.640 --> 31:00.640 On the assumption that they're actually rigid, 31:00.640 --> 31:02.240 but that's not true. 31:02.240 --> 31:03.720 If you're flying really quickly, 31:03.720 --> 31:06.920 these effects become significant. 31:06.920 --> 31:09.240 If you're flying close to the ground, 31:09.240 --> 31:12.160 you get pushed off by the ground, right? 31:12.160 --> 31:14.920 Something which every pilot knows when he tries to land 31:14.920 --> 31:18.000 or she tries to land, this is called a ground effect. 31:18.920 --> 31:21.000 Something very few pilots think about 31:21.000 --> 31:23.040 is what happens when you go close to a ceiling 31:23.040 --> 31:25.320 or you get sucked into a ceiling. 31:25.320 --> 31:26.880 There are very few aircrafts 31:26.880 --> 31:29.520 that fly close to any kind of ceiling. 31:29.520 --> 31:33.520 Likewise, when you go close to a wall, 31:33.520 --> 31:35.720 there are these wall effects. 31:35.720 --> 31:37.680 And if you've gone on a train 31:37.680 --> 31:39.600 and you pass another train that's traveling 31:39.600 --> 31:42.400 in the opposite direction, you feel the buffeting. 31:42.400 --> 31:45.400 And so these kinds of microclimates 31:45.400 --> 31:47.880 affect our UAV significantly. 31:47.880 --> 31:48.720 So if you want... 31:48.720 --> 31:50.640 And they're impossible to model, essentially. 31:50.640 --> 31:52.480 I wouldn't say they're impossible to model, 31:52.480 --> 31:54.880 but the level of sophistication you would need 31:54.880 --> 31:58.600 in the model and the software would be tremendous. 32:00.000 --> 32:02.920 Plus, to get everything right would be awfully tedious. 32:02.920 --> 32:05.080 So the way we do this is over time, 32:05.080 --> 32:09.000 we figure out how to adapt to these conditions. 32:10.360 --> 32:13.160 So early on, we use the form of learning 32:13.160 --> 32:15.760 that we call iterative learning. 32:15.760 --> 32:18.600 So this idea, if you want to perform a task, 32:18.600 --> 32:22.120 there are a few things that you need to change 32:22.120 --> 32:24.960 and iterate over a few parameters 32:24.960 --> 32:29.280 that over time you can figure out. 32:29.280 --> 32:33.400 So I could call it policy gradient reinforcement learning, 32:33.400 --> 32:34.920 but actually it was just iterative learning. 32:34.920 --> 32:36.000 Iterative learning. 32:36.000 --> 32:37.800 And so this was there way back. 32:37.800 --> 32:39.440 I think what's interesting is, 32:39.440 --> 32:41.640 if you look at autonomous vehicles today, 32:43.120 --> 32:45.680 learning occurs, could occur in two pieces. 32:45.680 --> 32:47.960 One is perception, understanding the world. 32:47.960 --> 32:50.080 Second is action, taking actions. 32:50.080 --> 32:52.240 Everything that I've seen that is successful 32:52.240 --> 32:54.360 is on the perception side of things. 32:54.360 --> 32:55.400 So in computer vision, 32:55.400 --> 32:57.840 we've made amazing strides in the last 10 years. 32:57.840 --> 33:01.640 So recognizing objects, actually detecting objects, 33:01.640 --> 33:06.400 classifying them and tagging them in some sense, 33:06.400 --> 33:07.440 annotating them. 33:07.440 --> 33:09.640 This is all done through machine learning. 33:09.640 --> 33:12.160 On the action side, on the other hand, 33:12.160 --> 33:13.720 I don't know of any examples 33:13.720 --> 33:15.560 where there are fielded systems 33:15.560 --> 33:17.560 where we actually learn 33:17.560 --> 33:20.560 the right behavior. 33:20.560 --> 33:22.760 Outside of single demonstration is successful. 33:22.760 --> 33:24.640 In the laboratory, this is the holy grail. 33:24.640 --> 33:26.040 Can you do end to end learning? 33:26.040 --> 33:28.800 Can you go from pixels to motor currents? 33:30.200 --> 33:31.600 This is really, really hard. 33:32.800 --> 33:35.080 And I think if you go forward, 33:35.080 --> 33:37.600 the right way to think about these things 33:37.600 --> 33:40.720 is data driven approaches, 33:40.720 --> 33:42.400 learning based approaches, 33:42.400 --> 33:45.280 in concert with model based approaches, 33:45.280 --> 33:47.320 which is the traditional way of doing things. 33:47.320 --> 33:48.720 So I think there's a piece, 33:48.720 --> 33:51.400 there's a role for each of these methodologies. 33:51.400 --> 33:52.440 So what do you think, 33:52.440 --> 33:53.880 just jumping out on topic 33:53.880 --> 33:56.200 since you mentioned autonomous vehicles, 33:56.200 --> 33:58.480 what do you think are the limits on the perception side? 33:58.480 --> 34:01.080 So I've talked to Elon Musk 34:01.080 --> 34:03.320 and there on the perception side, 34:03.320 --> 34:05.960 they're using primarily computer vision 34:05.960 --> 34:08.080 to perceive the environment. 34:08.080 --> 34:09.760 In your work with, 34:09.760 --> 34:12.560 because you work with the real world a lot 34:12.560 --> 34:13.720 and the physical world, 34:13.720 --> 34:15.800 what are the limits of computer vision? 34:15.800 --> 34:18.000 Do you think we can solve autonomous vehicles 34:19.160 --> 34:20.880 on the perception side, 34:20.880 --> 34:24.240 focusing on vision alone and machine learning? 34:24.240 --> 34:27.480 So, we also have a spinoff company, 34:27.480 --> 34:31.840 Exxon Technologies that works underground in mines. 34:31.840 --> 34:35.600 So you go into mines, they're dark, they're dirty. 34:36.480 --> 34:38.600 You fly in a dirty area, 34:38.600 --> 34:41.120 there's stuff you kick up from by the propellers, 34:41.120 --> 34:42.720 the downwash kicks up dust. 34:42.720 --> 34:45.520 I challenge you to get a computer vision algorithm 34:45.520 --> 34:46.680 to work there. 34:46.680 --> 34:49.600 So we use LIDARs in that setting. 34:51.200 --> 34:55.360 Indoors and even outdoors when we fly through fields, 34:55.360 --> 34:57.120 I think there's a lot of potential 34:57.120 --> 34:59.960 for just solving the problem using computer vision alone. 35:01.240 --> 35:02.760 But I think the bigger question is, 35:02.760 --> 35:06.160 can you actually solve 35:06.160 --> 35:09.440 or can you actually identify all the corner cases 35:09.440 --> 35:13.920 using a single sensing modality and using learning alone? 35:13.920 --> 35:15.400 So what's your intuition there? 35:15.400 --> 35:17.920 So look, if you have a corner case 35:17.920 --> 35:20.000 and your algorithm doesn't work, 35:20.000 --> 35:23.200 your instinct is to go get data about the corner case 35:23.200 --> 35:26.640 and patch it up, learn how to deal with that corner case. 35:27.640 --> 35:32.040 But at some point, this is gonna saturate, 35:32.040 --> 35:34.200 this approach is not viable. 35:34.200 --> 35:38.000 So today, computer vision algorithms can detect 35:38.000 --> 35:41.360 90% of the objects or can detect objects 90% of the time, 35:41.360 --> 35:43.920 classify them 90% of the time. 35:43.920 --> 35:47.960 Cats on the internet probably can do 95%, I don't know. 35:47.960 --> 35:52.520 But to get from 90% to 99%, you need a lot more data. 35:52.520 --> 35:54.480 And then I tell you, well, that's not enough 35:54.480 --> 35:56.680 because I have a safety critical application, 35:56.680 --> 36:00.160 I wanna go from 99% to 99.9%. 36:00.160 --> 36:01.600 That's even more data. 36:01.600 --> 36:08.600 So I think if you look at wanting accuracy on the X axis 36:09.600 --> 36:14.080 and look at the amount of data on the Y axis, 36:14.080 --> 36:16.440 I believe that curve is an exponential curve. 36:16.440 --> 36:19.480 Wow, okay, it's even hard if it's linear. 36:19.480 --> 36:20.800 It's hard if it's linear, totally, 36:20.800 --> 36:22.560 but I think it's exponential. 36:22.560 --> 36:24.120 And the other thing you have to think about 36:24.120 --> 36:29.600 is that this process is a very, very power hungry process 36:29.600 --> 36:32.880 to run data farms or servers. 36:32.880 --> 36:34.600 Power, do you mean literally power? 36:34.600 --> 36:36.600 Literally power, literally power. 36:36.600 --> 36:41.760 So in 2014, five years ago, and I don't have more recent data, 36:41.760 --> 36:48.360 2% of US electricity consumption was from data farms. 36:48.360 --> 36:52.080 So we think about this as an information science 36:52.080 --> 36:54.240 and information processing problem. 36:54.240 --> 36:57.840 Actually, it is an energy processing problem. 36:57.840 --> 37:00.440 And so unless we figured out better ways of doing this, 37:00.440 --> 37:02.440 I don't think this is viable. 37:02.440 --> 37:06.600 So talking about driving, which is a safety critical application 37:06.600 --> 37:10.440 and some aspect of flight is safety critical, 37:10.440 --> 37:12.960 maybe philosophical question, maybe an engineering one, 37:12.960 --> 37:15.000 what problem do you think is harder to solve, 37:15.000 --> 37:18.120 autonomous driving or autonomous flight? 37:18.120 --> 37:19.920 That's a really interesting question. 37:19.920 --> 37:25.440 I think autonomous flight has several advantages 37:25.440 --> 37:29.360 that autonomous driving doesn't have. 37:29.360 --> 37:32.400 So look, if I want to go from point A to point B, 37:32.400 --> 37:34.320 I have a very, very safe trajectory. 37:34.320 --> 37:36.800 Go vertically up to a maximum altitude, 37:36.800 --> 37:39.480 fly horizontally to just about the destination, 37:39.480 --> 37:42.400 and then come down vertically. 37:42.400 --> 37:45.400 This is preprogrammed. 37:45.400 --> 37:48.040 The equivalent of that is very hard to find 37:48.040 --> 37:51.560 in the self driving car world because you're on the ground, 37:51.560 --> 37:53.560 you're in a two dimensional surface, 37:53.560 --> 37:56.680 and the trajectories on the two dimensional surface 37:56.680 --> 38:00.200 are more likely to encounter obstacles. 38:00.200 --> 38:03.280 I mean this in an intuitive sense, but mathematically true. 38:03.280 --> 38:06.360 That's mathematically as well, that's true. 38:06.360 --> 38:10.040 There's other option on the 2G space of platooning, 38:10.040 --> 38:11.640 or because there's so many obstacles, 38:11.640 --> 38:13.280 you can connect with those obstacles 38:13.280 --> 38:14.560 and all these kind of options. 38:14.560 --> 38:16.560 Sure, but those exist in the three dimensional space as well. 38:16.560 --> 38:17.560 So they do. 38:17.560 --> 38:21.800 So the question also implies how difficult are obstacles 38:21.800 --> 38:23.800 in the three dimensional space in flight? 38:23.800 --> 38:25.600 So that's the downside. 38:25.600 --> 38:26.920 I think in three dimensional space, 38:26.920 --> 38:29.080 you're modeling three dimensional world, 38:29.080 --> 38:31.280 not just because you want to avoid it, 38:31.280 --> 38:33.040 but you want to reason about it, 38:33.040 --> 38:35.360 and you want to work in the three dimensional environment, 38:35.360 --> 38:37.480 and that's significantly harder. 38:37.480 --> 38:38.920 So that's one disadvantage. 38:38.920 --> 38:41.040 I think the second disadvantage is of course, 38:41.040 --> 38:43.200 anytime you fly, you have to put up 38:43.200 --> 38:46.560 with the peculiarities of aerodynamics 38:46.560 --> 38:48.720 and their complicated environments. 38:48.720 --> 38:49.800 How do you negotiate that? 38:49.800 --> 38:51.880 So that's always a problem. 38:51.880 --> 38:55.240 Do you see a time in the future where there is, 38:55.240 --> 38:58.720 you mentioned there's agriculture applications. 38:58.720 --> 39:01.680 So there's a lot of applications of flying robots, 39:01.680 --> 39:03.040 but do you see a time in the future 39:03.040 --> 39:05.360 where there's tens of thousands, 39:05.360 --> 39:08.160 or maybe hundreds of thousands of delivery drones 39:08.160 --> 39:12.160 that fill the sky, delivery flying robots? 39:12.160 --> 39:14.200 I think there's a lot of potential 39:14.200 --> 39:15.920 for the last mile delivery. 39:15.920 --> 39:19.240 And so in crowded cities, I don't know, 39:19.240 --> 39:21.400 if you go to a place like Hong Kong, 39:21.400 --> 39:24.400 just crossing the river can take half an hour, 39:24.400 --> 39:29.400 and while a drone can just do it in five minutes at most. 39:29.400 --> 39:34.400 I think you look at delivery of supplies to remote villages. 39:35.800 --> 39:38.680 I work with a nonprofit called Weave Robotics. 39:38.680 --> 39:40.920 So they work in the Peruvian Amazon, 39:40.920 --> 39:44.680 where the only highways that are available 39:44.680 --> 39:47.440 are the only highways or rivers. 39:47.440 --> 39:52.440 And to get from point A to point B may take five hours, 39:52.960 --> 39:55.600 while with a drone, you can get there in 30 minutes. 39:56.680 --> 39:59.880 So just delivering drugs, 39:59.880 --> 40:04.880 retrieving samples for testing vaccines, 40:05.160 --> 40:07.120 I think there's huge potential here. 40:07.120 --> 40:09.960 So I think the challenges are not technological, 40:09.960 --> 40:12.040 but the challenge is economical. 40:12.040 --> 40:15.560 The one thing I'll tell you that nobody thinks about 40:15.560 --> 40:18.920 is the fact that we've not made huge strides 40:18.920 --> 40:20.840 in battery technology. 40:20.840 --> 40:23.520 Yes, it's true, batteries are becoming less expensive 40:23.520 --> 40:26.240 because we have these mega factories that are coming up, 40:26.240 --> 40:28.800 but they're all based on lithium based technologies. 40:28.800 --> 40:31.480 And if you look at the energy density 40:31.480 --> 40:33.240 and the power density, 40:33.240 --> 40:38.000 those are two fundamentally limiting numbers. 40:38.000 --> 40:39.680 So power density is important 40:39.680 --> 40:42.480 because for a UAV to take off vertically into the air, 40:42.480 --> 40:46.360 which most drones do, they don't have a runway, 40:46.360 --> 40:50.240 you consume roughly 200 watts per kilo at the small size. 40:51.560 --> 40:53.920 That's a lot, right? 40:53.920 --> 40:57.520 In contrast, the human brain consumes less than 80 watts, 40:57.520 --> 40:58.920 the whole of the human brain. 40:59.920 --> 41:03.600 So just imagine just lifting yourself into the air 41:03.600 --> 41:06.000 is like two or three light bulbs, 41:06.000 --> 41:07.840 which makes no sense to me. 41:07.840 --> 41:10.440 Yeah, so you're going to have to at scale 41:10.440 --> 41:12.880 solve the energy problem then, 41:12.880 --> 41:17.880 charging the batteries, storing the energy and so on. 41:18.920 --> 41:20.680 And then the storage is the second problem, 41:20.680 --> 41:22.960 but storage limits the range. 41:22.960 --> 41:27.960 But you have to remember that you have to burn 41:28.680 --> 41:31.600 a lot of it per given time. 41:31.600 --> 41:32.920 So the burning is another problem. 41:32.920 --> 41:34.640 Which is a power question. 41:34.640 --> 41:38.640 Yes, and do you think just your intuition, 41:38.640 --> 41:43.640 there are breakthroughs in batteries on the horizon? 41:44.960 --> 41:46.440 How hard is that problem? 41:46.440 --> 41:47.600 Look, there are a lot of companies 41:47.600 --> 41:52.600 that are promising flying cars that are autonomous 41:53.880 --> 41:55.120 and that are clean. 41:59.400 --> 42:01.680 I think they're over promising. 42:01.680 --> 42:04.800 The autonomy piece is doable. 42:04.800 --> 42:07.040 The clean piece, I don't think so. 42:08.000 --> 42:11.840 There's another company that I work with called JetOptra. 42:11.840 --> 42:14.360 They make small jet engines. 42:15.760 --> 42:18.080 And they can get up to 50 miles an hour very easily 42:18.080 --> 42:19.960 and lift 50 kilos. 42:19.960 --> 42:22.840 But they're jet engines, they're efficient, 42:23.920 --> 42:26.320 they're a little louder than electric vehicles, 42:26.320 --> 42:28.960 but they can build flying cars. 42:28.960 --> 42:32.440 So your sense is that there's a lot of pieces 42:32.440 --> 42:33.520 that have come together. 42:33.520 --> 42:37.360 So on this crazy question, 42:37.360 --> 42:39.720 if you look at companies like Kitty Hawk, 42:39.720 --> 42:42.080 working on electric, so the clean, 42:43.880 --> 42:45.840 talking to Sebastian Thrun, right? 42:45.840 --> 42:48.840 It's a crazy dream, you know? 42:48.840 --> 42:52.080 But you work with flight a lot. 42:52.080 --> 42:55.760 You've mentioned before that manned flights 42:55.760 --> 43:00.760 or carrying a human body is very difficult to do. 43:01.640 --> 43:04.240 So how crazy is flying cars? 43:04.240 --> 43:05.400 Do you think there'll be a day 43:05.400 --> 43:10.400 when we have vertical takeoff and landing vehicles 43:11.080 --> 43:14.040 that are sufficiently affordable 43:14.960 --> 43:17.440 that we're going to see a huge amount of them? 43:17.440 --> 43:19.680 And they would look like something like we dream of 43:19.680 --> 43:21.080 when we think about flying cars. 43:21.080 --> 43:22.200 Yeah, like the Jetsons. 43:22.200 --> 43:23.160 The Jetsons, yeah. 43:23.160 --> 43:25.560 So look, there are a lot of smart people working on this 43:25.560 --> 43:29.640 and you never say something is not possible 43:29.640 --> 43:32.200 when you have people like Sebastian Thrun working on it. 43:32.200 --> 43:35.160 So I totally think it's viable. 43:35.160 --> 43:38.240 I question, again, the electric piece. 43:38.240 --> 43:39.520 The electric piece, yeah. 43:39.520 --> 43:41.680 And again, for short distances, you can do it. 43:41.680 --> 43:43.640 And there's no reason to suggest 43:43.640 --> 43:45.840 that these all just have to be rotorcrafts. 43:45.840 --> 43:46.920 You take off vertically, 43:46.920 --> 43:49.680 but then you morph into a forward flight. 43:49.680 --> 43:51.600 I think there are a lot of interesting designs. 43:51.600 --> 43:56.040 The question to me is, are these economically viable? 43:56.040 --> 43:59.160 And if you agree to do this with fossil fuels, 43:59.160 --> 44:01.960 it instantly immediately becomes viable. 44:01.960 --> 44:03.480 That's a real challenge. 44:03.480 --> 44:06.560 Do you think it's possible for robots and humans 44:06.560 --> 44:08.840 to collaborate successfully on tasks? 44:08.840 --> 44:13.640 So a lot of robotics folks that I talk to and work with, 44:13.640 --> 44:18.000 I mean, humans just add a giant mess to the picture. 44:18.000 --> 44:20.320 So it's best to remove them from consideration 44:20.320 --> 44:22.400 when solving specific tasks. 44:22.400 --> 44:23.600 It's very difficult to model. 44:23.600 --> 44:26.000 There's just a source of uncertainty. 44:26.000 --> 44:31.000 In your work with these agile flying robots, 44:32.560 --> 44:35.680 do you think there's a role for collaboration with humans? 44:35.680 --> 44:38.600 Or is it best to model tasks in a way 44:38.600 --> 44:43.400 that doesn't have a human in the picture? 44:43.400 --> 44:46.760 Well, I don't think we should ever think about robots 44:46.760 --> 44:48.120 without human in the picture. 44:48.120 --> 44:50.960 Ultimately, robots are there because we want them 44:50.960 --> 44:54.360 to solve problems for humans. 44:54.360 --> 44:58.280 But there's no general solution to this problem. 44:58.280 --> 45:00.000 I think if you look at human interaction 45:00.000 --> 45:02.400 and how humans interact with robots, 45:02.400 --> 45:05.280 you know, we think of these in sort of three different ways. 45:05.280 --> 45:07.600 One is the human commanding the robot. 45:08.880 --> 45:12.880 The second is the human collaborating with the robot. 45:12.880 --> 45:15.520 So for example, we work on how a robot 45:15.520 --> 45:18.720 can actually pick up things with a human and carry things. 45:18.720 --> 45:20.880 That's like true collaboration. 45:20.880 --> 45:25.000 And third, we think about humans as bystanders, 45:25.000 --> 45:27.240 self driving cars, what's the human's role 45:27.240 --> 45:30.320 and how do self driving cars 45:30.320 --> 45:32.920 acknowledge the presence of humans? 45:32.920 --> 45:35.840 So I think all of these things are different scenarios. 45:35.840 --> 45:38.480 It depends on what kind of humans, what kind of task. 45:39.640 --> 45:41.840 And I think it's very difficult to say 45:41.840 --> 45:45.520 that there's a general theory that we all have for this. 45:45.520 --> 45:48.440 But at the same time, it's also silly to say 45:48.440 --> 45:52.000 that we should think about robots independent of humans. 45:52.000 --> 45:55.760 So to me, human robot interaction 45:55.760 --> 45:59.760 is almost a mandatory aspect of everything we do. 45:59.760 --> 46:02.440 Yes, but to which degree, so your thoughts, 46:02.440 --> 46:05.240 if we jump to autonomous vehicles, for example, 46:05.240 --> 46:08.680 there's a big debate between what's called 46:08.680 --> 46:10.640 level two and level four. 46:10.640 --> 46:13.680 So semi autonomous and autonomous vehicles. 46:13.680 --> 46:16.440 And so the Tesla approach currently at least 46:16.440 --> 46:18.960 has a lot of collaboration between human and machine. 46:18.960 --> 46:22.040 So the human is supposed to actively supervise 46:22.040 --> 46:23.880 the operation of the robot. 46:23.880 --> 46:28.880 Part of the safety definition of how safe a robot is 46:29.160 --> 46:32.880 in that case is how effective is the human in monitoring it. 46:32.880 --> 46:37.880 Do you think that's ultimately not a good approach 46:37.880 --> 46:42.360 in sort of having a human in the picture, 46:42.360 --> 46:47.360 not as a bystander or part of the infrastructure, 46:47.400 --> 46:50.000 but really as part of what's required 46:50.000 --> 46:51.560 to make the system safe? 46:51.560 --> 46:53.720 This is harder than it sounds. 46:53.720 --> 46:58.200 I think, you know, if you, I mean, 46:58.200 --> 47:01.360 I'm sure you've driven before in highways and so on. 47:01.360 --> 47:06.120 It's really very hard to have to relinquish control 47:06.120 --> 47:10.440 to a machine and then take over when needed. 47:10.440 --> 47:12.280 So I think Tesla's approach is interesting 47:12.280 --> 47:14.800 because it allows you to periodically establish 47:14.800 --> 47:18.520 some kind of contact with the car. 47:18.520 --> 47:20.640 Toyota, on the other hand, is thinking about 47:20.640 --> 47:24.800 shared autonomy or collaborative autonomy as a paradigm. 47:24.800 --> 47:27.480 If I may argue, these are very, very simple ways 47:27.480 --> 47:29.680 of human robot collaboration, 47:29.680 --> 47:31.880 because the task is pretty boring. 47:31.880 --> 47:35.000 You sit in a vehicle, you go from point A to point B. 47:35.000 --> 47:37.360 I think the more interesting thing to me is, 47:37.360 --> 47:38.760 for example, search and rescue. 47:38.760 --> 47:41.980 I've got a human first responder, robot first responders. 47:43.160 --> 47:45.120 I gotta do something. 47:45.120 --> 47:46.000 It's important. 47:46.000 --> 47:47.800 I have to do it in two minutes. 47:47.800 --> 47:49.240 The building is burning. 47:49.240 --> 47:50.440 There's been an explosion. 47:50.440 --> 47:51.360 It's collapsed. 47:51.360 --> 47:52.800 How do I do it? 47:52.800 --> 47:54.740 I think to me, those are the interesting things 47:54.740 --> 47:57.160 where it's very, very unstructured. 47:57.160 --> 47:58.480 And what's the role of the human? 47:58.480 --> 48:00.200 What's the role of the robot? 48:00.200 --> 48:02.440 Clearly, there's lots of interesting challenges 48:02.440 --> 48:03.440 and there's a field. 48:03.440 --> 48:05.760 I think we're gonna make a lot of progress in this area. 48:05.760 --> 48:07.600 Yeah, it's an exciting form of collaboration. 48:07.600 --> 48:08.440 You're right. 48:08.440 --> 48:11.120 In autonomous driving, the main enemy 48:11.120 --> 48:13.120 is just boredom of the human. 48:13.120 --> 48:13.960 Yes. 48:13.960 --> 48:15.680 As opposed to in rescue operations, 48:15.680 --> 48:18.360 it's literally life and death. 48:18.360 --> 48:22.080 And the collaboration enables 48:22.080 --> 48:23.820 the effective completion of the mission. 48:23.820 --> 48:24.760 So it's exciting. 48:24.760 --> 48:27.400 In some sense, we're also doing this. 48:27.400 --> 48:30.520 You think about the human driving a car 48:30.520 --> 48:33.800 and almost invariably, the human's trying 48:33.800 --> 48:35.000 to estimate the state of the car, 48:35.000 --> 48:37.280 they estimate the state of the environment and so on. 48:37.280 --> 48:40.120 But what if the car were to estimate the state of the human? 48:40.120 --> 48:41.960 So for example, I'm sure you have a smartphone 48:41.960 --> 48:44.580 and the smartphone tries to figure out what you're doing 48:44.580 --> 48:48.320 and send you reminders and oftentimes telling you 48:48.320 --> 48:49.540 to drive to a certain place, 48:49.540 --> 48:51.400 although you have no intention of going there 48:51.400 --> 48:53.880 because it thinks that that's where you should be 48:53.880 --> 48:56.240 because of some Gmail calendar entry 48:57.520 --> 48:58.960 or something like that. 48:58.960 --> 49:01.600 And it's trying to constantly figure out who you are, 49:01.600 --> 49:02.740 what you're doing. 49:02.740 --> 49:04.200 If a car were to do that, 49:04.200 --> 49:06.840 maybe that would make the driver safer 49:06.840 --> 49:08.160 because the car is trying to figure out 49:08.160 --> 49:09.760 is the driver paying attention, 49:09.760 --> 49:11.600 looking at his or her eyes, 49:12.480 --> 49:14.400 looking at circadian movements. 49:14.400 --> 49:16.480 So I think the potential is there, 49:16.480 --> 49:18.600 but from the reverse side, 49:18.600 --> 49:21.640 it's not robot modeling, but it's human modeling. 49:21.640 --> 49:22.880 It's more on the human, right. 49:22.880 --> 49:25.320 And I think the robots can do a very good job 49:25.320 --> 49:29.120 of modeling humans if you really think about the framework 49:29.120 --> 49:32.640 that you have a human sitting in a cockpit, 49:32.640 --> 49:35.820 surrounded by sensors, all staring at him, 49:35.820 --> 49:37.860 in addition to be staring outside, 49:37.860 --> 49:39.160 but also staring at him. 49:39.160 --> 49:40.960 I think there's a real synergy there. 49:40.960 --> 49:42.360 Yeah, I love that problem 49:42.360 --> 49:45.560 because it's the new 21st century form of psychology, 49:45.560 --> 49:48.520 actually AI enabled psychology. 49:48.520 --> 49:51.280 A lot of people have sci fi inspired fears 49:51.280 --> 49:54.080 of walking robots like those from Boston Dynamics. 49:54.080 --> 49:56.480 If you just look at shows on Netflix and so on, 49:56.480 --> 49:59.040 or flying robots like those you work with, 49:59.920 --> 50:03.160 how would you, how do you think about those fears? 50:03.160 --> 50:05.040 How would you alleviate those fears? 50:05.040 --> 50:09.040 Do you have inklings, echoes of those same concerns? 50:09.040 --> 50:11.760 You know, anytime we develop a technology 50:11.760 --> 50:14.160 meaning to have positive impact in the world, 50:14.160 --> 50:15.780 there's always the worry that, 50:17.440 --> 50:21.000 you know, somebody could subvert those technologies 50:21.000 --> 50:23.280 and use it in an adversarial setting. 50:23.280 --> 50:25.280 And robotics is no exception, right? 50:25.280 --> 50:29.280 So I think it's very easy to weaponize robots. 50:29.280 --> 50:30.880 I think we talk about swarms. 50:31.720 --> 50:33.960 One thing I worry a lot about is, 50:33.960 --> 50:35.880 so, you know, for us to get swarms to work 50:35.880 --> 50:38.280 and do something reliably, it's really hard. 50:38.280 --> 50:42.040 But suppose I have this challenge 50:42.040 --> 50:44.360 of trying to destroy something, 50:44.360 --> 50:45.720 and I have a swarm of robots, 50:45.720 --> 50:47.280 where only one out of the swarm 50:47.280 --> 50:48.920 needs to get to its destination. 50:48.920 --> 50:52.640 So that suddenly becomes a lot more doable. 50:52.640 --> 50:54.720 And so I worry about, you know, 50:54.720 --> 50:56.920 this general idea of using autonomy 50:56.920 --> 50:58.600 with lots and lots of agents. 51:00.040 --> 51:01.320 I mean, having said that, look, 51:01.320 --> 51:03.760 a lot of this technology is not very mature. 51:03.760 --> 51:05.520 My favorite saying is that 51:06.560 --> 51:10.520 if somebody had to develop this technology, 51:10.520 --> 51:12.320 wouldn't you rather the good guys do it? 51:12.320 --> 51:13.880 So the good guys have a good understanding 51:13.880 --> 51:15.560 of the technology, so they can figure out 51:15.560 --> 51:18.320 how this technology is being used in a bad way, 51:18.320 --> 51:21.360 or could be used in a bad way and try to defend against it. 51:21.360 --> 51:22.760 So we think a lot about that. 51:22.760 --> 51:25.400 So we have, we're doing research 51:25.400 --> 51:28.240 on how to defend against swarms, for example. 51:28.240 --> 51:29.600 That's interesting. 51:29.600 --> 51:32.960 There's in fact a report by the National Academies 51:32.960 --> 51:35.520 on counter UAS technologies. 51:36.680 --> 51:38.200 This is a real threat, 51:38.200 --> 51:40.320 but we're also thinking about how to defend against this 51:40.320 --> 51:42.920 and knowing how swarms work. 51:42.920 --> 51:47.160 Knowing how autonomy works is, I think, very important. 51:47.160 --> 51:49.280 So it's not just politicians? 51:49.280 --> 51:51.640 Do you think engineers have a role in this discussion? 51:51.640 --> 51:52.480 Absolutely. 51:52.480 --> 51:55.280 I think the days where politicians 51:55.280 --> 51:57.680 can be agnostic to technology are gone. 51:59.200 --> 52:02.640 I think every politician needs to be 52:03.840 --> 52:05.680 literate in technology. 52:05.680 --> 52:08.640 And I often say technology is the new liberal art. 52:09.800 --> 52:12.920 Understanding how technology will change your life, 52:12.920 --> 52:14.480 I think is important. 52:14.480 --> 52:18.080 And every human being needs to understand that. 52:18.080 --> 52:20.160 And maybe we can elect some engineers 52:20.160 --> 52:22.720 to office as well on the other side. 52:22.720 --> 52:24.840 What are the biggest open problems in robotics? 52:24.840 --> 52:27.760 And you said we're in the early days in some sense. 52:27.760 --> 52:31.040 What are the problems we would like to solve in robotics? 52:31.040 --> 52:32.520 I think there are lots of problems, right? 52:32.520 --> 52:36.440 But I would phrase it in the following way. 52:36.440 --> 52:39.520 If you look at the robots we're building, 52:39.520 --> 52:43.160 they're still very much tailored towards 52:43.160 --> 52:46.520 doing specific tasks and specific settings. 52:46.520 --> 52:49.480 I think the question of how do you get them to operate 52:49.480 --> 52:51.080 in much broader settings 52:53.560 --> 52:58.040 where things can change in unstructured environments 52:58.040 --> 52:59.160 is up in the air. 52:59.160 --> 53:01.200 So think of self driving cars. 53:02.920 --> 53:05.680 Today, we can build a self driving car in a parking lot. 53:05.680 --> 53:09.000 We can do level five autonomy in a parking lot. 53:10.040 --> 53:13.240 But can you do a level five autonomy 53:13.240 --> 53:16.840 in the streets of Napoli in Italy or Mumbai in India? 53:16.840 --> 53:17.760 No. 53:17.760 --> 53:22.400 So in some sense, when we think about robotics, 53:22.400 --> 53:25.120 we have to think about where they're functioning, 53:25.120 --> 53:27.760 what kind of environment, what kind of a task. 53:27.760 --> 53:29.800 We have no understanding 53:29.800 --> 53:32.800 of how to put both those things together. 53:32.800 --> 53:34.000 So we're in the very early days 53:34.000 --> 53:35.920 of applying it to the physical world. 53:35.920 --> 53:38.800 And I was just in Naples actually. 53:38.800 --> 53:42.200 And there's levels of difficulty and complexity 53:42.200 --> 53:45.880 depending on which area you're applying it to. 53:45.880 --> 53:46.720 I think so. 53:46.720 --> 53:49.320 And we don't have a systematic way of understanding that. 53:51.040 --> 53:53.800 Everybody says, just because a computer 53:53.800 --> 53:56.520 can now beat a human at any board game, 53:56.520 --> 53:59.920 we certainly know something about intelligence. 53:59.920 --> 54:01.360 That's not true. 54:01.360 --> 54:04.400 A computer board game is very, very structured. 54:04.400 --> 54:08.480 It is the equivalent of working in a Henry Ford factory 54:08.480 --> 54:11.680 where things, parts come, you assemble, move on. 54:11.680 --> 54:14.120 It's a very, very, very structured setting. 54:14.120 --> 54:15.680 That's the easiest thing. 54:15.680 --> 54:17.040 And we know how to do that. 54:18.400 --> 54:20.400 So you've done a lot of incredible work 54:20.400 --> 54:23.720 at the UPenn, University of Pennsylvania, GraspLab. 54:23.720 --> 54:26.560 You're now Dean of Engineering at UPenn. 54:26.560 --> 54:31.320 What advice do you have for a new bright eyed undergrad 54:31.320 --> 54:34.640 interested in robotics or AI or engineering? 54:34.640 --> 54:36.560 Well, I think there's really three things. 54:36.560 --> 54:40.600 One is you have to get used to the idea 54:40.600 --> 54:42.840 that the world will not be the same in five years 54:42.840 --> 54:45.160 or four years whenever you graduate, right? 54:45.160 --> 54:46.120 Which is really hard to do. 54:46.120 --> 54:48.960 So this thing about predicting the future, 54:48.960 --> 54:50.520 every one of us needs to be trying 54:50.520 --> 54:52.360 to predict the future always. 54:53.280 --> 54:54.960 Not because you'll be any good at it, 54:54.960 --> 54:56.440 but by thinking about it, 54:56.440 --> 55:00.880 I think you sharpen your senses and you become smarter. 55:00.880 --> 55:02.080 So that's number one. 55:02.080 --> 55:05.760 Number two, it's a corollary of the first piece, 55:05.760 --> 55:09.360 which is you really don't know what's gonna be important. 55:09.360 --> 55:12.080 So this idea that I'm gonna specialize in something 55:12.080 --> 55:15.320 which will allow me to go in a particular direction, 55:15.320 --> 55:16.480 it may be interesting, 55:16.480 --> 55:18.480 but it's important also to have this breadth 55:18.480 --> 55:20.360 so you have this jumping off point. 55:22.000 --> 55:23.000 I think the third thing, 55:23.000 --> 55:25.360 and this is where I think Penn excels. 55:25.360 --> 55:27.240 I mean, we teach engineering, 55:27.240 --> 55:29.960 but it's always in the context of the liberal arts. 55:29.960 --> 55:32.360 It's always in the context of society. 55:32.360 --> 55:35.840 As engineers, we cannot afford to lose sight of that. 55:35.840 --> 55:37.640 So I think that's important. 55:37.640 --> 55:39.960 But I think one thing that people underestimate 55:39.960 --> 55:40.920 when they do robotics 55:40.920 --> 55:43.440 is the importance of mathematical foundations, 55:43.440 --> 55:46.880 the importance of representations. 55:47.720 --> 55:50.040 Not everything can just be solved 55:50.040 --> 55:52.440 by looking for Ross packages on the internet 55:52.440 --> 55:56.280 or to find a deep neural network that works. 55:56.280 --> 55:59.080 I think the representation question is key, 55:59.080 --> 56:00.400 even to machine learning, 56:00.400 --> 56:05.400 where if you ever hope to achieve or get to explainable AI, 56:05.400 --> 56:07.760 somehow there need to be representations 56:07.760 --> 56:09.080 that you can understand. 56:09.080 --> 56:11.120 So if you wanna do robotics, 56:11.120 --> 56:12.680 you should also do mathematics. 56:12.680 --> 56:15.080 And you said liberal arts, a little literature. 56:16.160 --> 56:17.200 If you wanna build a robot, 56:17.200 --> 56:19.320 it should be reading Dostoyevsky. 56:19.320 --> 56:20.360 I agree with that. 56:20.360 --> 56:21.200 Very good. 56:21.200 --> 56:23.560 So Vijay, thank you so much for talking today. 56:23.560 --> 56:24.400 It was an honor. 56:24.400 --> 56:25.240 Thank you. 56:25.240 --> 56:26.200 It was just a very exciting conversation. 56:26.200 --> 56:46.200 Thank you.