vtt/episode_037_small.vtt

WEBVTT

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 The following is a conversation with Vijay Kumar.

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 He's one of the top roboticists in the world,

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 a professor at the University of Pennsylvania,

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 a Dean of Penn Engineering,

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 former director of Grasp Lab,

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 or the General Robotics Automation Sensing

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 and Perception Laboratory at Penn,

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 that was established back in 1979, that's 40 years ago.

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 Vijay is perhaps best known

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 for his work in multi robot systems, robot swarms,

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 and micro aerial vehicles.

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 Robots that elegantly cooperate in flight

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 under all the uncertainty and challenges

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 that the real world conditions present.

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 This is the Artificial Intelligence Podcast.

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 If you enjoy it, subscribe on YouTube,

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 give it five stars on iTunes,

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 support it on Patreon,

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 or simply connect with me on Twitter

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 at Lex Freedman spelled FRID MAN.

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 And now here's my conversation with Vijay Kumar.

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 What is the first robot you've ever built

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 or were a part of building?

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 Way back when I was in graduate school,

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 I was part of a fairly big project

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 that involved building a very large hexapod.

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 This weighed close to 7,000 pounds.

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 And it was powered by hydraulic actuation,

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 or was actuated by hydraulics with 18 motors,

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 hydraulic motors, each controlled by an Intel 8085 processor

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 and an 8086 co processor.

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 And so imagine this huge monster that had 18 joints,

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 each controlled by an independent computer.

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 And there was a 19th computer

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 that actually did the coordination

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 between these 18 joints.

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 So as part of this project,

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 and my thesis work was,

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 how do you coordinate the 18 legs?

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 And in particular, the pressures in the hydraulic cylinders

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 to get efficient locomotion.

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 It sounds like a giant mess.

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 So how difficult is it to make all the motors communicate?

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 Presumably you have to send signals

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 hundreds of times a second, or at least...

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 This was not my work, but the folks who worked on this

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 wrote what I believe to be

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 the first multiprocessor operating system.

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 This was in the 80s.

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 And you had to make sure that obviously messages

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 got across from one joint to another.

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 You have to remember the clock speeds on those computers

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 were about half a megahertz.

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 Right.

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 So the 80s.

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 So not to romanticize the notion,

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 but how did it make you feel to make,

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 to see that robot move?

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 It was amazing.

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 In hindsight, it looks like, well,

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 we built the thing which really should have been much smaller.

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 And of course, today's robots are much smaller.

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 You look at, you know, Boston Dynamics,

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 our ghost robotics has been off from pen.

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 But back then, you were stuck

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 with the substrate you had, the compute you had,

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 so things were unnecessarily big.

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 But at the same time, and this is just human psychology,

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 somehow bigger means grander.

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 You know, people never have the same appreciation

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 for nanotechnology or nano devices

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 as they do for the space shuttle or the Boeing 747.

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 Yeah, you've actually done quite a good job

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 at illustrating that small is beautiful

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 in terms of robotics.

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 So what is on that topic is the most beautiful

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 or elegant robot emotion that you've ever seen.

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 Not to pick favorites or whatever,

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 but something that just inspires you that you remember.

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 Well, I think the thing that I'm most proud of

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 that my students have done is really think about

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 small UAVs that can maneuver and constrain spaces

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 and in particular, their ability to coordinate

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 with each other and form three dimensional patterns.

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 So once you can do that,

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 you can essentially create 3D objects in the sky

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 and you can deform these objects on the fly.

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 So in some sense, your toolbox of what you can create

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 has suddenly got enhanced.

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 And before that, we did the two dimensional version of this.

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 So we had ground robots forming patterns and so on.

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 So that was not as impressive, that was not as beautiful.

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 But if you do it in 3D, suspend it in midair

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 and you've got to go back to 2011 when we did this.

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 Now it's actually pretty standard to do these things

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 eight years later, but back then it was a big accomplishment.

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 So the distributed cooperation

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 is where beauty emerges in your eyes?

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 Well, I think beauty to an engineer is very different

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 from beauty to someone who's looking at robots

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 from the outside, if you will.

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 But what I meant there, so before we said that grand

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 is associated with size.

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 And another way of thinking about this

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 is just the physical shape and the idea

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 that you can create physical shapes in midair

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 and have them deform, that's beautiful.

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 But the individual components,

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 the agility is beautiful too, right?

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 That is true too.

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 So then how quickly can you actually manipulate

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 these three dimensional shapes

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 and the individual components?

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 Yes, you're right.

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 Oh, by the way, said UAV, unmanned aerial vehicle.

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 What's a good term for drones, UAVs, quadcopters?

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 Is there a term that's being standardized?

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 I don't know if there is.

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 Everybody wants to use the word drones.

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 And I've often said there's drones to me

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 is a pejorative word.

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 It signifies something that's dumb,

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 a pre program that does one little thing

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 and robots are anything but drones.

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 So I actually don't like that word,

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 but that's what everybody uses.

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 You could call it unpiloted.

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 Unpiloted.

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 But even unpiloted could be radio controlled,

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 could be remotely controlled in many different ways.

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 And I think the right word

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 is thinking about it as an aerial robot.

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 You also say agile, autonomous aerial robot, right?

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 Yeah, so agility is an attribute,

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 but they don't have to be.

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 So what biological system,

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 because you've also drawn a lot of inspiration

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 with those I've seen bees and ants

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 that you've talked about, what living creatures

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 have you found to be most inspiring

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 as an engineer, instructive in your work in robotics?

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 To me, so ants are really quite incredible creatures, right?

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 So you, I mean, the individuals arguably are very simple

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 in how they're built, and yet they're incredibly resilient

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 as a population.

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 And as individuals, they're incredibly robust.

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 So, if you take an ant with six legs, you remove one leg,

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 it still works just fine.

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 And it moves along,

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 and I don't know that he even realizes it's lost a leg.

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 So that's the robustness at the individual ant level.

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 But then you look about this instinct

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 for self preservation of the colonies,

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 and they adapt in so many amazing ways,

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 you know, transcending gaps by just chaining themselves

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 together when you have a flood,

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 being able to recruit other teammates

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 to carry big morsels of food,

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 and then going out in different directions,

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 looking for food,

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 and then being able to demonstrate consensus,

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 even though they don't communicate directly

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 with each other the way we communicate with each other,

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 in some sense, they also know how to do democracy,

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 probably better than what we do.

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 Yeah, somehow, even democracy is emergent.

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 It seems like all of the phenomena

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 that we see is all emergent.

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 It seems like there's no centralized communicator.

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 There is, so I think a lot is made about that word, emergent,

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 and it means lots of things to different people,

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 but you're absolutely right.

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 I think as an engineer, you think about what element,

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 elemental behaviors, what primitives you could synthesize

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 so that the whole looks incredibly powerful,

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 incredibly synergistic,

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 the whole definitely being greater than some of the parts,

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 and ants are living proof of that.

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 So when you see these beautiful swarms

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 where there's biological systems of robots,

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 do you sometimes think of them

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 as a single individual living intelligent organism?

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 So it's the same as thinking of our human civilization

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 as one organism, or do you still, as an engineer,

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 think about the individual components

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 and all the engineering that went into

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 the individual components?

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 Well, that's very interesting.

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 So again, philosophically, as engineers,

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 what we want to do is to go beyond the individual components,

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 the individual units, and think about it as a unit,

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 as a cohesive unit,

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 without worrying about the individual components.

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 If you start obsessing about the individual building blocks

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 and what they do, you inevitably will find it hard

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 to scale up.

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 Just mathematically,

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 just think about individual things you want to model,

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 and if you want to have 10 of those,

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 then you essentially are taking Cartesian products

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 of 10 things, and that makes it really complicated

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 than to do any kind of synthesis or design

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 in that high dimension space is really hard.

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 So the right way to do this

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 is to think about the individuals in a clever way

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 so that at the higher level,

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 when you look at lots and lots of them,

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 abstractly, you can think of them

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 in some low dimensional space.

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 So what does that involve?

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 For the individual, you have to try to make

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 the way they see the world as local as possible,

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 and the other thing,

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 do you just have to make them robust to collisions?

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 Like you said, with the ants,

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 if something fails, the whole swarm doesn't fail.

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 Right, I think as engineers, we do this.

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 I mean, you know, think about,

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 we build planes or we build iPhones,

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 and we know that by taking individual components,

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 well engineered components,

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 with well specified interfaces

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 that behave in a predictable way,

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 you can build complex systems.

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 So that's ingrained I would claim

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 in most engineers thinking,

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 and it's true for computer scientists as well.

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 I think what's different here is that you want

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 the individuals to be robust in some sense,

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 as we do in these other settings,

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 but you also want some degree of resiliency

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 for the population.

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 And so you really want them to be able

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 to reestablish communication with their neighbors.

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 You want them to rethink their strategy

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 for group behavior.

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 You want them to reorganize.

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 And that's where I think a lot of the challenges lie.

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 So just at a high level,

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 what does it take for a bunch of,

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 what should we call them,

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 flying robots to create a formation?

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 Just for people who are not familiar with robotics

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 in general, how much information is needed?

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 How do you even make it happen

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 without a centralized controller?

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 So I mean, there are a couple of different ways

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 of looking at this.

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 If you are a purist, you think of it as a way

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 of recreating what nature does.

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 So nature forms groups for several reasons,

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 but mostly it's because of this instinct

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 that organisms have of preserving their colonies,

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 their population, which means what?

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 You need shelter, you need food, you need to procreate,

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 and that's basically it.

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 So the kinds of interactions you see are all organic.

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 They're all local.

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 And the only information that they share,

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 and mostly it's indirectly,

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 is to again preserve the herd or the flock

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 or the swarm and either by looking for new sources of food

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 or looking for new shelters, right?

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 As engineers, when we build swarms, we have a mission.

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 And when you think of a mission,

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 and it involves mobility,

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 most often it's described in some kind

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 of a global coordinate system.

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 As a human, as an operator, as a commander,

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 or as a collaborator, I have my coordinate system

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 and I want the robots to be consistent with that.

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 So I might think of it slightly differently.

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 I might want the robots to recognize that coordinate system,

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 which means not only do they have to think locally

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 in terms of who their immediate neighbors are,

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 but they have to be cognizant

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 of what the global environment looks like.

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 So if I go, if I say surround this building

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 and protect this from intruders,

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 well, they're immediately in a building

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 centered coordinate system

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 and I have to tell them where the building is.

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 And they're globally collaborating

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 on the map of that building.

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 They're maintaining some kind of global,

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 not just in the frame of the building,

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 but there's information that's ultimately being built up

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 explicitly as opposed to kind of implicitly,

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 like nature might.

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 Correct, correct.

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 So in some sense, nature is very, very sophisticated,

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 but the tasks that nature solves

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 or needs to solve are very different

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 from the kind of engineered tasks,

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 artificial tasks that we are forced to address.

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 And again, there's nothing preventing us

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 from solving these other problems,

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 but ultimately it's about impact.

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 You want these swarms to do something useful.

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 And so you're kind of driven into this very unnatural,

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 if you will, unnatural meaning, not like how nature does,

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 setting.

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 And it's probably a little bit more expensive

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 to do it the way nature does,

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 because nature is less sensitive to the loss of the individual

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 and cost wise in robotics,

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 I think you're more sensitive to losing individuals.

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 I think that's true, although if you look at the price

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 to performance ratio of robotic components,

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 it's coming down dramatically.

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 I'm interested.

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 Right, it continues to come down.

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 So I think we're asymptotically approaching the point

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 where we would get, yeah,

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 the cost of individuals would really become insignificant.

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 So let's step back at a high level view,

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 the impossible question of what kind of,

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 as an overview, what kind of autonomous flying vehicles

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 are there in general?

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 I think the ones that receive a lot of notoriety

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 are obviously the military vehicles.

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 Military vehicles are controlled by a base station,

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 but have a lot of human supervision,

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 but have limited autonomy,

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 which is the ability to go from point A to point B,

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 and even the more sophisticated vehicles

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 can do autonomous takeoff and landing.

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 And those usually have wings and they're heavy?

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 Usually they're wings,

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 but there's nothing preventing us from doing this

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 for helicopters as well.

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 There are many military organizations that have

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 autonomous helicopters in the same vein.

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 And by the way, you look at autopilots and airplanes,

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 and it's actually very similar.

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 In fact, one interesting question we can ask is,

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 if you look at all the air safety violations,

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 all the crashes that occurred,

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 would they have happened if the plane

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 were truly autonomous, and I think you'll find

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 that in many of the cases,

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 because of pilot error, we made silly decisions.

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 And so in some sense, even in air traffic,

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 commercial air traffic, there's a lot of applications,

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 although we only see autonomy being enabled

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 at very high altitudes when the plane is an autopilot.

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 There's still a role for the human,

16:42.520 --> 16:47.520
 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 --> 16:52.600
 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,

17:04.600 --> 17:09.600
 can we make robots that will be able to make decisions

17:09.600 --> 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've driven in a city, try to use GPS navigation,

17:30.480 --> 17:32.760
 tall buildings, you immediately lose GPS.

17:33.720 --> 17:36.320
 And so that's not a very sophisticated way

17:36.320 --> 17:37.880
 of building autonomy.

17:37.880 --> 17:39.560
 I think the second piece of infrastructure

17:39.560 --> 17:41.920
 that I rely on is communications.

17:41.920 --> 17:46.200
 Again, it's very easy to jam communications.

17:47.400 --> 17:49.680
 In fact, if you use Wi Fi,

17:49.680 --> 17:51.880
 you know that Wi Fi signals drop out,

17:51.880 --> 17:53.560
 cell signals drop out.

17:53.560 --> 17:56.840
 So to rely on something like that is not good.

17:58.600 --> 18:01.240
 The third form of infrastructure we use,

18:01.240 --> 18:02.960
 and I hate to call it infrastructure,

18:02.960 --> 18:06.400
 but it is that in the sense of robots, it's people.

18:06.400 --> 18:08.760
 So you could rely on somebody to pilot you.

18:08.760 --> 18:09.960
 Right.

18:09.960 --> 18:11.600
 And so the question you wanna ask is

18:11.600 --> 18:13.400
 if there are no pilots,

18:13.400 --> 18:16.200
 if there's no communications with any base station,

18:16.200 --> 18:18.720
 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 --> 18:28.280
 a priori model of what might happen in the future.

18:28.280 --> 18:29.560
 Can robots navigate?

18:29.560 --> 18:31.440
 So that is true autonomy.

18:31.440 --> 18:33.240
 So that's true autonomy.

18:33.240 --> 18:35.040
 And we're talking about, you mentioned

18:35.040 --> 18:36.880
 like military applications and drones.

18:36.880 --> 18:38.280
 Okay, so what else is there?

18:38.280 --> 18:43.280
 You talk about agile autonomous flying robots, aerial robots.

18:43.480 --> 18:46.320
 So that's a different kind of, it's not winged,

18:46.320 --> 18:48.120
 it's not big, at least it's small.

18:48.120 --> 18:50.800
 So I use the word agility mostly,

18:50.800 --> 18:53.480
 or at least we're motivated to do agile robots,

18:53.480 --> 18:57.960
 mostly because robots can operate

18:57.960 --> 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 --> 19:28.880
 or to count fruits to measure the tree trunks.

19:31.240 --> 19:33.240
 These are things we do, by the way.

19:33.240 --> 19:35.920
 Yeah, some cool agriculture stuff you've shown in the past,

19:35.920 --> 19:36.760
 it's really awesome.

19:36.760 --> 19:40.400
 So in those kinds of settings, you do need that agility.

19:40.400 --> 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 --> 19:48.040
 What it really means is you see the unexpected

19:48.040 --> 19:51.520
 and you're able to maneuver in a safe way,

19:51.520 --> 19:55.440
 and in a way that gets you the most information

19:55.440 --> 19:57.720
 about the thing you're trying to do.

19:57.720 --> 20:00.520
 By the way, you may be the only person

20:00.520 --> 20:04.280
 who in a TED talk has used a math equation,

20:04.280 --> 20:07.720
 which is amazing, people should go see one of your TED talks.

20:07.720 --> 20:08.840
 Actually, it's very interesting

20:08.840 --> 20:13.560
 because the TED curator, Chris Anderson, told me,

20:13.560 --> 20:15.400
 you can't show math.

20:15.400 --> 20:18.240
 And I thought about it, but that's who I am.

20:18.240 --> 20:20.800
 I mean, that's our work.

20:20.800 --> 20:25.800
 And so I felt compelled to give the audience a taste

20:25.800 --> 20:27.680
 for at least some math.

20:27.680 --> 20:32.680
 So on that point, simply, what does it take

20:32.680 --> 20:37.120
 to make a thing with four motors fly, a quadcopter,

20:37.120 --> 20:40.400
 one of these little flying robots?

20:41.560 --> 20:43.800
 How hard is it to make it fly?

20:43.800 --> 20:46.360
 How do you coordinate the four motors?

20:47.360 --> 20:52.360
 How do you convert those motors into actual movement?

20:52.400 --> 20:54.600
 So this is an interesting question.

20:54.600 --> 20:57.840
 We've been trying to do this since 2000.

20:57.840 --> 21:00.360
 It is a commentary on the sensors

21:00.360 --> 21:01.880
 that were available back then,

21:01.880 --> 21:04.320
 and the computers that were available back then.

21:05.640 --> 21:08.080
 And a number of things happened

21:08.080 --> 21:10.320
 between 2000 and 2007.

21:11.640 --> 21:15.560
 One is the advances in computing, which is,

21:15.560 --> 21:16.840
 so we all know about Moore's Law,

21:16.840 --> 21:19.760
 but I think 2007 was a tipping point,

21:19.760 --> 21:22.800
 the year of the iPhone, the year of the cloud.

21:22.800 --> 21:24.720
 Lots of things happened in 2007.

21:25.680 --> 21:27.640
 But going back even further,

21:27.640 --> 21:31.440
 inertial measurement units as a sensor really matured.

21:31.440 --> 21:33.040
 Again, lots of reasons for that.

21:34.000 --> 21:35.480
 Certainly there's a lot of federal funding,

21:35.480 --> 21:37.440
 particularly DARPA in the US,

21:38.360 --> 21:42.840
 but they didn't anticipate this boom in IMUs.

21:43.800 --> 21:46.080
 But if you look subsequently,

21:46.080 --> 21:49.000
 what happened is that every car manufacturer

21:49.000 --> 21:50.120
 had to put an airbag in,

21:50.120 --> 21:52.720
 which meant you had to have an accelerometer on board.

21:52.720 --> 21:54.080
 And so that drove down the price

21:54.080 --> 21:56.280
 to performance ratio of the sensors.

21:56.280 --> 21:57.960
 I should know this, that's very interesting.

21:57.960 --> 21:59.480
 It's very interesting, the connection there.

21:59.480 --> 22:03.160
 And that's why research is very hard to predict the outcomes.

22:04.920 --> 22:07.760
 And again, the federal government spent a ton of money

22:07.760 --> 22:12.360
 on things that they thought were useful for resonators,

22:12.360 --> 22:16.920
 but it ended up enabling these small UAVs, which is great,

22:16.920 --> 22:18.600
 because I could have never raised that much money

22:18.600 --> 22:20.800
 and told, sold this project,

22:20.800 --> 22:22.280
 hey, we want to build these small UAVs.

22:22.280 --> 22:25.520
 Can you actually fund the development of low cost IMUs?

22:25.520 --> 22:27.720
 So why do you need an IMU on an IMU?

22:27.720 --> 22:30.440
 So I'll come back to that,

22:30.440 --> 22:33.400
 but so in 2007, 2008, we were able to build these.

22:33.400 --> 22:35.280
 And then the question you're asking was a good one,

22:35.280 --> 22:40.280
 how do you coordinate the motors to develop this?

22:40.320 --> 22:43.920
 But over the last 10 years, everything is commoditized.

22:43.920 --> 22:47.920
 A high school kid today can pick up a Raspberry Pi kit

22:49.520 --> 22:50.600
 and build this,

22:50.600 --> 22:53.240
 all the low levels functionality is all automated.

22:53.240 --> 22:58.240
 But basically at some level, you have to drive the motors

22:59.160 --> 23:03.660
 at the right RPMs, the right velocity,

23:04.560 --> 23:07.480
 in order to generate the right amount of thrust

23:07.480 --> 23:09.960
 in order to position it and orient it

23:09.960 --> 23:12.840
 in a way 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 --> 23:36.480
 or velocity information.

23:37.440 --> 23:39.320
 For example, when we walk,

23:39.320 --> 23:41.520
 we implicitly have this information

23:41.520 --> 23:45.800
 because we kind of know how, what our stride length is.

23:45.800 --> 23:50.800
 We also are looking at images fly past our retina,

23:51.440 --> 23:54.240
 if you will, and so we can estimate velocity.

23:54.240 --> 23:56.280
 We also have accelerometers in our head

23:56.280 --> 23:59.120
 and we're able to integrate all these pieces of information

23:59.120 --> 24:02.320
 to determine where we are as we walk.

24:02.320 --> 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 --> 24:11.600
 or other sensor that's measuring velocity.

24:11.600 --> 24:15.800
 And then you need some kind of a global reference frame

24:15.800 --> 24:19.480
 if you really want to think about doing something

24:19.480 --> 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 --> 24:32.680
 is what allows you to, first of all, fly and hover

24:32.680 --> 24:35.640
 and then you can change the orientation

24:35.640 --> 24:37.640
 and the velocity and so on.

24:37.640 --> 24:38.480
 Exactly, exactly.

24:38.480 --> 24:40.360
 So there's a bunch of degrees of freedom

24:40.360 --> 24:42.240
 or there's six degrees of freedom

24:42.240 --> 24:44.960
 but you only have four inputs, the four motors.

24:44.960 --> 24:49.960
 And it turns out to be a remarkably versatile configuration.

24:50.960 --> 24:53.120
 You think at first, well, I only have four motors,

24:53.120 --> 24:55.040
 how do I go sideways?

24:55.040 --> 24:56.360
 But it's not too hard to say, well,

24:56.360 --> 24:59.200
 if I tilt myself, I can go sideways.

24:59.200 --> 25:01.200
 And then you have four motors pointing up,

25:01.200 --> 25:05.400
 how do I rotate in place about a vertical axis?

25:05.400 --> 25:07.840
 Well, you rotate them at different speeds

25:07.840 --> 25:09.760
 and that generates reaction moments

25:09.760 --> 25:11.560
 and that allows you to turn.

25:11.560 --> 25:13.400
 So it's actually a pretty,

25:13.400 --> 25:17.040
 it's an optimal configuration from an engineer standpoint.

25:17.960 --> 25:22.960
 It's very simple, very cleverly done and very versatile.

25:23.800 --> 25:26.520
 So if you could step back to a time,

25:27.320 --> 25:30.120
 so I've always known flying robots as,

25:31.120 --> 25:35.840
 to me it was natural that the quadcopters should fly.

25:35.840 --> 25:38.040
 But when you first started working with it,

25:38.040 --> 25:42.040
 how surprised are you that you can make,

25:42.040 --> 25:45.560
 do so much with the four motors?

25:45.560 --> 25:47.640
 How surprising is that you can make this thing fly,

25:47.640 --> 25:49.800
 first of all, that you can make it hover,

25:49.800 --> 25:52.040
 then you can add control to it?

25:52.920 --> 25:55.800
 Firstly, this is not, the four motor configuration

25:55.800 --> 26:00.120
 is not ours, it has at least a hundred year history.

26:01.080 --> 26:02.480
 And various people,

26:02.480 --> 26:06.280
 various people try to get quadrotors to fly

26:06.280 --> 26:08.120
 without much success.

26:09.240 --> 26:11.560
 As I said, we've been working on this since 2000.

26:11.560 --> 26:13.360
 Our first designs were,

26:13.360 --> 26:15.160
 well, this is way too complicated.

26:15.160 --> 26:19.160
 Why not we try to get an omnidirectional flying robot?

26:19.160 --> 26:22.760
 So our early designs, we had eight rotors.

26:22.760 --> 26:26.080
 And so these eight rotors were arranged uniformly

26:27.520 --> 26:28.880
 on a sphere, if you will.

26:28.880 --> 26:31.360
 So you can imagine a symmetric configuration

26:31.360 --> 26:34.160
 and so you should be able to fly anywhere.

26:34.160 --> 26:36.280
 But the real challenge we had is the strength

26:36.280 --> 26:37.880
 to weight ratio is not enough,

26:37.880 --> 26:41.240
 and of course we didn't have the sensors and so on.

26:41.240 --> 26:43.840
 So everybody knew, or at least the people

26:43.840 --> 26:45.680
 who worked with rotor crafts knew,

26:45.680 --> 26:47.320
 four rotors would get it done.

26:48.280 --> 26:50.200
 So that was not our idea.

26:50.200 --> 26:53.480
 But it took a while before we could actually do

26:53.480 --> 26:56.520
 the onboard sensing and the computation

26:56.520 --> 27:00.400
 that was needed for the kinds of agile maneuvering

27:00.400 --> 27:03.800
 that we wanted to do in our little aerial robots.

27:03.800 --> 27:08.320
 And that only happened between 2007 and 2009 in our lab.

27:08.320 --> 27:10.680
 Yeah, and you have to send the signal

27:10.680 --> 27:13.200
 maybe a hundred times a second.

27:13.200 --> 27:15.400
 So the compute there is everything

27:15.400 --> 27:16.720
 has to come down in price.

27:16.720 --> 27:21.720
 And what are the steps of getting from point A to point B?

27:22.320 --> 27:25.840
 So we just talked about like local control,

27:25.840 --> 27:30.840
 but if all the kind of cool dancing in the air

27:30.840 --> 27:34.480
 that I've seen you show, how do you make it happen?

27:34.480 --> 27:38.840
 Make it trajectory, first of all, okay,

27:38.840 --> 27:41.600
 figure out a trajectory, so plan a trajectory,

27:41.600 --> 27:44.320
 and then how do you make that trajectory happen?

27:44.320 --> 27:47.320
 I think planning is a very fundamental problem in robotics.

27:47.320 --> 27:50.120
 I think 10 years ago it was an esoteric thing,

27:50.120 --> 27:52.360
 but today with self driving cars,

27:52.360 --> 27:55.160
 everybody can understand this basic idea

27:55.160 --> 27:57.320
 that a car sees a whole bunch of things

27:57.320 --> 27:59.720
 and it has to keep a lane or maybe make a right turn

27:59.720 --> 28:02.160
 or switch lanes, it has to plan a trajectory,

28:02.160 --> 28:04.320
 it has to be safe, it has to be efficient.

28:04.320 --> 28:06.120
 So everybody's familiar with that.

28:06.120 --> 28:07.400
 That's kind of the first step

28:07.400 --> 28:12.400
 that you have to think about when you say autonomy.

28:14.320 --> 28:18.600
 And so for us, it's about finding smooth motions,

28:18.600 --> 28:20.760
 motions that are safe.

28:20.760 --> 28:22.360
 So we think about these two things.

28:22.360 --> 28:24.160
 One is optimality, one is safety.

28:24.160 --> 28:26.720
 Clearly you cannot compromise safety.

28:26.720 --> 28:30.160
 So you're looking for safe, optimal motions.

28:30.160 --> 28:33.160
 The other thing you have to think about

28:33.160 --> 28:37.360
 is can you actually compute a reasonable trajectory

28:37.360 --> 28:41.560
 in a small amount of time, because you have a time budget.

28:41.560 --> 28:44.360
 So the optimal becomes suboptimal,

28:44.360 --> 28:49.360
 but in our lab we focus on synthesizing smooth trajectory

28:50.560 --> 28:52.360
 that satisfy all the constraints.

28:52.360 --> 28:57.200
 In other words, don't violate any safety constraints

28:57.200 --> 29:02.200
 and is as efficient as possible.

29:02.200 --> 29:04.600
 And when I say efficient, it could mean

29:04.600 --> 29:07.760
 I want to get from point A to point B as quickly as possible

29:07.760 --> 29:11.200
 or I want to get to it as gracefully as possible

29:11.200 --> 29:15.360
 or I want to consume as little energy as possible.

29:15.360 --> 29:17.600
 But always staying within the safety constraints.

29:17.600 --> 29:22.440
 But yes, always finding a safe trajectory.

29:22.440 --> 29:24.440
 So there's a lot of excitement and progress

29:24.440 --> 29:26.440
 in the field of machine learning.

29:26.440 --> 29:31.440
 And reinforcement learning and the neural network variant

29:31.440 --> 29:33.440
 of that with deeper reinforcement learning.

29:33.440 --> 29:37.440
 Do you see a role of machine learning in...

29:37.440 --> 29:40.040
 So a lot of the success with flying robots

29:40.040 --> 29:41.840
 did not rely on machine learning,

29:41.840 --> 29:44.440
 except for maybe a little bit of the perception

29:44.440 --> 29:46.040
 on the computer vision side.

29:46.040 --> 29:48.040
 On the control side and the planning,

29:48.040 --> 29:50.040
 do you see there's a role in the future

29:50.040 --> 29:51.040
 for machine learning?

29:51.040 --> 29:53.040
 So let me disagree a little bit with you.

29:53.040 --> 29:56.040
 I think we never perhaps called out in my work

29:56.040 --> 29:59.040
 called out learning, but even this very simple idea

29:59.040 --> 30:04.040
 of being able to fly through a constrained space.

30:04.040 --> 30:07.040
 The first time you try it, you'll invariably...

30:07.040 --> 30:10.040
 You might get it wrong if the task is challenging.

30:10.040 --> 30:14.040
 And the reason is to get it perfectly right,

30:14.040 --> 30:17.040
 you have to model everything in the environment.

30:17.040 --> 30:22.040
 And flying is notoriously hard to model.

30:22.040 --> 30:28.040
 There are aerodynamic effects that we constantly discover,

30:28.040 --> 30:31.040
 even just before I was talking to you,

30:31.040 --> 30:37.040
 I was talking to a student about how blades flap when they fly.

30:37.040 --> 30:43.040
 And that ends up changing how a rotorcraft

30:43.040 --> 30:46.040
 is accelerated in the angular direction.

30:46.040 --> 30:48.040
 Does it use like microflaps or something?

30:48.040 --> 30:49.040
 It's not microflaps.

30:49.040 --> 30:52.040
 We assume that each blade is rigid,

30:52.040 --> 30:54.040
 but actually it flaps a little bit.

30:54.040 --> 30:55.040
 It bends.

30:55.040 --> 30:56.040
 Interesting, yeah.

30:56.040 --> 30:58.040
 And so the models rely on the fact,

30:58.040 --> 31:01.040
 on the assumption that they're actually rigid.

31:01.040 --> 31:02.040
 But that's not true.

31:02.040 --> 31:04.040
 If you're flying really quickly,

31:04.040 --> 31:07.040
 these effects become significant.

31:07.040 --> 31:09.040
 If you're flying close to the ground,

31:09.040 --> 31:12.040
 you get pushed off by the ground.

31:12.040 --> 31:15.040
 Something which every pilot knows when he tries to land

31:15.040 --> 31:19.040
 or she tries to land, this is called a ground effect.

31:19.040 --> 31:21.040
 Something very few pilots think about

31:21.040 --> 31:23.040
 is what happens when you go close to a ceiling,

31:23.040 --> 31:25.040
 or you get sucked into a ceiling.

31:25.040 --> 31:29.040
 There are very few aircraft that fly close to any kind of ceiling.

31:29.040 --> 31:33.040
 Likewise, when you go close to a wall,

31:33.040 --> 31:36.040
 there are these wall effects.

31:36.040 --> 31:39.040
 And if you've gone on a train and you pass another train

31:39.040 --> 31:41.040
 that's traveling the opposite direction,

31:41.040 --> 31:43.040
 you can feel the buffeting.

31:43.040 --> 31:46.040
 And so these kinds of microclimates

31:46.040 --> 31:48.040
 affect our UAVs significantly.

31:48.040 --> 31:51.040
 And they're impossible to model, essentially.

31:51.040 --> 31:53.040
 I wouldn't say they're impossible to model,

31:53.040 --> 31:55.040
 but the level of sophistication you would need

31:55.040 --> 32:00.040
 in the model and the software would be tremendous.

32:00.040 --> 32:03.040
 Plus, to get everything right would be awfully tedious.

32:03.040 --> 32:05.040
 So the way we do this is over time,

32:05.040 --> 32:10.040
 we figure out how to adapt to these conditions.

32:10.040 --> 32:13.040
 So early on, we use the form of learning

32:13.040 --> 32:15.040
 that we call iterative learning.

32:15.040 --> 32:18.040
 So this idea, if you want to perform a task,

32:18.040 --> 32:23.040
 there are a few things that you need to change

32:23.040 --> 32:25.040
 and iterate over a few parameters

32:25.040 --> 32:29.040
 that over time you can figure out.

32:29.040 --> 32:34.040
 So I could call it policy gradient reinforcement learning,

32:34.040 --> 32:36.040
 but actually it was just iterative learning.

32:36.040 --> 32:38.040
 And so this was there way back.

32:38.040 --> 32:40.040
 I think what's interesting is,

32:40.040 --> 32:43.040
 if you look at autonomous vehicles today,

32:43.040 --> 32:46.040
 learning occurs, could occur in two pieces.

32:46.040 --> 32:48.040
 One is perception, understanding the world.

32:48.040 --> 32:51.040
 Second is action, taking actions.

32:51.040 --> 32:54.040
 Everything that I've seen that is successful

32:54.040 --> 32:56.040
 is on the perception side of things.

32:56.040 --> 32:59.040
 So in computer vision, we've made amazing strides

32:59.040 --> 33:00.040
 in the last 10 years.

33:00.040 --> 33:03.040
 So recognizing objects, actually detecting objects,

33:03.040 --> 33:08.040
 classifying them and tagging them in some sense,

33:08.040 --> 33:11.040
 annotating them, this is all done through machine learning.

33:11.040 --> 33:13.040
 On the action side, on the other hand,

33:13.040 --> 33:17.040
 I don't know if any examples where there are fielded systems

33:17.040 --> 33:21.040
 where we actually learn the right behavior.

33:21.040 --> 33:23.040
 Outside of single demonstration is successful.

33:23.040 --> 33:25.040
 On the laboratory, this is the Holy Grail.

33:25.040 --> 33:27.040
 Can you do end to end learning?

33:27.040 --> 33:31.040
 Can you go from pixels to motor currents?

33:31.040 --> 33:33.040
 This is really, really hard.

33:33.040 --> 33:36.040
 And I think if you go forward,

33:36.040 --> 33:38.040
 the right way to think about these things

33:38.040 --> 33:43.040
 is data driven approaches, learning based approaches,

33:43.040 --> 33:46.040
 in concert with model based approaches,

33:46.040 --> 33:48.040
 which is the traditional way of doing things.

33:48.040 --> 33:50.040
 So I think there's a piece,

33:50.040 --> 33:52.040
 there's a role for each of these methodologies.

33:52.040 --> 33:55.040
 So what do you think, just jumping out on topics,

33:55.040 --> 33:57.040
 since you mentioned autonomous vehicles,

33:57.040 --> 33:59.040
 what do you think are the limits on the perception side?

33:59.040 --> 34:02.040
 So I've talked to Elon Musk,

34:02.040 --> 34:04.040
 and there on the perception side,

34:04.040 --> 34:07.040
 they're using primarily computer vision

34:07.040 --> 34:09.040
 to perceive the environment.

34:09.040 --> 34:13.040
 In your work with, because you work with the real world a lot,

34:13.040 --> 34:15.040
 and the physical world,

34:15.040 --> 34:17.040
 what are the limits of computer vision?

34:17.040 --> 34:20.040
 Do you think you can solve autonomous vehicles,

34:20.040 --> 34:22.040
 focusing on the perception side,

34:22.040 --> 34:25.040
 focusing on vision alone and machine learning?

34:25.040 --> 34:29.040
 So we also have a spin off company, Exxon Technologies,

34:29.040 --> 34:32.040
 that works underground in mines.

34:32.040 --> 34:35.040
 So you go into mines, they're dark.

34:35.040 --> 34:37.040
 They're dirty.

34:37.040 --> 34:39.040
 You fly in a dirty area,

34:39.040 --> 34:42.040
 there's stuff you kick up by the propellers,

34:42.040 --> 34:44.040
 the downwash kicks up dust.

34:44.040 --> 34:48.040
 I challenge you to get a computer vision algorithm to work there.

34:48.040 --> 34:53.040
 So we use LIDARS in that setting.

34:53.040 --> 34:57.040
 Indoors, and even outdoors when we fly through fields,

34:57.040 --> 34:59.040
 I think there's a lot of potential

34:59.040 --> 35:03.040
 for just solving the problem using computer vision alone.

35:03.040 --> 35:05.040
 But I think the bigger question is,

35:05.040 --> 35:08.040
 can you actually solve,

35:08.040 --> 35:11.040
 or can you actually identify all the corner cases

35:11.040 --> 35:14.040
 using a single sensing modality

35:14.040 --> 35:16.040
 and using learning alone?

35:16.040 --> 35:18.040
 What's your intuition there?

35:18.040 --> 35:20.040
 So look, if you have a corner case

35:20.040 --> 35:22.040
 and your algorithm doesn't work,

35:22.040 --> 35:25.040
 your instinct is to go get data about the corner case

35:25.040 --> 35:29.040
 and patch it up, learn how to deal with that corner case.

35:29.040 --> 35:32.040
 But at some point,

35:32.040 --> 35:36.040
 this is going to saturate, this approach is not viable.

35:36.040 --> 35:39.040
 So today, computer vision algorithms

35:39.040 --> 35:43.040
 can detect objects 90% of the time,

35:43.040 --> 35:45.040
 classify them 90% of the time.

35:45.040 --> 35:49.040
 Cats on the internet probably can do 95%, I don't know.

35:49.040 --> 35:54.040
 But to get from 90% to 99%, you need a lot more data.

35:54.040 --> 35:56.040
 And then I tell you, well, that's not enough

35:56.040 --> 35:58.040
 because I have a safety critical application

35:58.040 --> 36:01.040
 that want to go from 99% to 99.9%,

36:01.040 --> 36:03.040
 well, that's even more data.

36:03.040 --> 36:07.040
 So I think if you look at

36:07.040 --> 36:11.040
 wanting accuracy on the x axis

36:11.040 --> 36:15.040
 and look at the amount of data on the y axis,

36:15.040 --> 36:18.040
 I believe that curve is an exponential curve.

36:18.040 --> 36:21.040
 Wow, okay, it's even hard if it's linear.

36:21.040 --> 36:24.040
 It's hard if it's linear, totally, but I think it's exponential.

36:24.040 --> 36:26.040
 And the other thing you have to think about

36:26.040 --> 36:31.040
 is that this process is a very, very power hungry process

36:31.040 --> 36:34.040
 to run data farms or servers.

36:34.040 --> 36:36.040
 Power, do you mean literally power?

36:36.040 --> 36:38.040
 Literally power, literally power.

36:38.040 --> 36:43.040
 So in 2014, five years ago, and I don't have more recent data,

36:43.040 --> 36:50.040
 2% of US electricity consumption was from data farms.

36:50.040 --> 36:54.040
 So we think about this as an information science

36:54.040 --> 36:56.040
 and information processing problem.

36:56.040 --> 36:59.040
 Actually, it is an energy processing problem.

36:59.040 --> 37:02.040
 And so unless we've figured out better ways of doing this,

37:02.040 --> 37:04.040
 I don't think this is viable.

37:04.040 --> 37:08.040
 So talking about driving, which is a safety critical application

37:08.040 --> 37:11.040
 and some aspect of the flight is safety critical,

37:11.040 --> 37:14.040
 maybe philosophical question, maybe an engineering one.

37:14.040 --> 37:16.040
 What problem do you think is harder to solve?

37:16.040 --> 37:19.040
 Autonomous driving or autonomous flight?

37:19.040 --> 37:21.040
 That's a really interesting question.

37:21.040 --> 37:26.040
 I think autonomous flight has several advantages

37:26.040 --> 37:30.040
 that autonomous driving doesn't have.

37:30.040 --> 37:33.040
 So look, if I want to go from point A to point B,

37:33.040 --> 37:35.040
 I have a very, very safe trajectory.

37:35.040 --> 37:38.040
 Go vertically up to a maximum altitude,

37:38.040 --> 37:41.040
 fly horizontally to just about the destination

37:41.040 --> 37:43.040
 and then come down vertically.

37:43.040 --> 37:46.040
 This is preprogrammed.

37:46.040 --> 37:49.040
 The equivalent of that is very hard to find

37:49.040 --> 37:53.040
 in a self driving car world because you're on the ground,

37:53.040 --> 37:55.040
 you're in a two dimensional surface,

37:55.040 --> 37:58.040
 and the trajectories on the two dimensional surface

37:58.040 --> 38:01.040
 are more likely to encounter obstacles.

38:01.040 --> 38:03.040
 I mean this in an intuitive sense,

38:03.040 --> 38:05.040
 but mathematically true, that's...

38:05.040 --> 38:08.040
 Mathematically as well, that's true.

38:08.040 --> 38:11.040
 There's other option on the 2G space of platooning

38:11.040 --> 38:13.040
 or because there's so many obstacles,

38:13.040 --> 38:15.040
 you can connect with those obstacles

38:15.040 --> 38:16.040
 and all these kinds of problems.

38:16.040 --> 38:18.040
 But those exist in the three dimensional space as well.

38:18.040 --> 38:19.040
 So they do.

38:19.040 --> 38:23.040
 So the question also implies how difficult are obstacles

38:23.040 --> 38:25.040
 in the three dimensional space in flight?

38:25.040 --> 38:27.040
 So that's the downside.

38:27.040 --> 38:29.040
 I think in three dimensional space,

38:29.040 --> 38:31.040
 you're modeling three dimensional world,

38:31.040 --> 38:33.040
 not just because you want to avoid it,

38:33.040 --> 38:35.040
 but you want to reason about it

38:35.040 --> 38:37.040
 and you want to work in that three dimensional environment.

38:37.040 --> 38:39.040
 And that's significantly harder.

38:39.040 --> 38:41.040
 So that's one disadvantage.

38:41.040 --> 38:43.040
 I think the second disadvantage is of course,

38:43.040 --> 38:45.040
 anytime you fly, you have to put up

38:45.040 --> 38:49.040
 with the peculiarities of aerodynamics

38:49.040 --> 38:51.040
 and their complicated environments.

38:51.040 --> 38:52.040
 How do you negotiate that?

38:52.040 --> 38:54.040
 So that's always a problem.

38:54.040 --> 38:57.040
 Do you see a time in the future where there is...

38:57.040 --> 39:00.040
 You mentioned there's agriculture applications.

39:00.040 --> 39:03.040
 So there's a lot of applications of flying robots.

39:03.040 --> 39:07.040
 But do you see a time in the future where there is tens of thousands

39:07.040 --> 39:10.040
 or maybe hundreds of thousands of delivery drones

39:10.040 --> 39:14.040
 that fill the sky, a delivery of flying robots?

39:14.040 --> 39:18.040
 I think there's a lot of potential for the last mile delivery.

39:18.040 --> 39:21.040
 And so in crowded cities,

39:21.040 --> 39:24.040
 I don't know if you go to a place like Hong Kong,

39:24.040 --> 39:27.040
 just crossing the river can take half an hour.

39:27.040 --> 39:32.040
 And while a drone can just do it in five minutes at most.

39:32.040 --> 39:38.040
 I think you look at delivery of supplies to remote villages.

39:38.040 --> 39:41.040
 I work with a nonprofit called Weave Robotics.

39:41.040 --> 39:43.040
 So they work in the Peruvian Amazon,

39:43.040 --> 39:47.040
 where the only highways are rivers.

39:47.040 --> 39:49.040
 And to get from point A to point B

39:49.040 --> 39:52.040
 may take five hours.

39:52.040 --> 39:56.040
 While with a drone, you can get there in 30 minutes.

39:56.040 --> 39:59.040
 So just delivering drugs,

39:59.040 --> 40:04.040
 retrieving samples for testing vaccines.

40:04.040 --> 40:06.040
 I think there's huge potential here.

40:06.040 --> 40:09.040
 So I think the challenges are not technological.

40:09.040 --> 40:12.040
 The challenge is economical.

40:12.040 --> 40:16.040
 The one thing I'll tell you that nobody thinks about

40:16.040 --> 40:21.040
 is the fact that we've not made huge strides in battery technology.

40:21.040 --> 40:22.040
 Yes, it's true.

40:22.040 --> 40:24.040
 Batteries are becoming less expensive

40:24.040 --> 40:27.040
 because we have these mega factories that are coming up.

40:27.040 --> 40:29.040
 But they're all based on lithium based technologies.

40:29.040 --> 40:34.040
 And if you look at the energy density and the power density,

40:34.040 --> 40:39.040
 those are two fundamentally limiting numbers.

40:39.040 --> 40:41.040
 So power density is important because for a UAV

40:41.040 --> 40:43.040
 to take off vertically into the air,

40:43.040 --> 40:47.040
 which most drones do, they don't have a runway,

40:47.040 --> 40:52.040
 you consume roughly 200 watts per kilo at the small size.

40:52.040 --> 40:54.040
 That's a lot.

40:54.040 --> 40:58.040
 In contrast, the human brain consumes less than 80 watts,

40:58.040 --> 41:00.040
 the whole of the human brain.

41:00.040 --> 41:04.040
 So just imagine just lifting yourself into the air

41:04.040 --> 41:08.040
 is like two or three light bulbs, which makes no sense to me.

41:08.040 --> 41:12.040
 Yeah, so you're going to have to at scale solve the energy problem

41:12.040 --> 41:19.040
 then charging the batteries, storing the energy and so on.

41:19.040 --> 41:21.040
 And then the storage is the second problem.

41:21.040 --> 41:23.040
 But storage limits the range.

41:23.040 --> 41:30.040
 But you have to remember that you have to burn a lot of it

41:30.040 --> 41:32.040
 for a given time.

41:32.040 --> 41:33.040
 So the burning is another problem.

41:33.040 --> 41:35.040
 Which is a power question.

41:35.040 --> 41:36.040
 Yes.

41:36.040 --> 41:39.040
 And do you think just your intuition,

41:39.040 --> 41:45.040
 there are breakthroughs in batteries on the horizon?

41:45.040 --> 41:47.040
 How hard is that problem?

41:47.040 --> 41:52.040
 Look, there are a lot of companies that are promising flying cars,

41:52.040 --> 42:00.040
 that are autonomous, and that are clean.

42:00.040 --> 42:02.040
 I think they're over promising.

42:02.040 --> 42:05.040
 The autonomy piece is doable.

42:05.040 --> 42:08.040
 The clean piece, I don't think so.

42:08.040 --> 42:12.040
 There's another company that I work with called Jatatra.

42:12.040 --> 42:16.040
 They make small jet engines.

42:16.040 --> 42:20.040
 And they can get up to 50 miles an hour very easily and lift 50 kilos.

42:20.040 --> 42:22.040
 But they're jet engines.

42:22.040 --> 42:24.040
 They're efficient.

42:24.040 --> 42:26.040
 They're a little louder than electric vehicles.

42:26.040 --> 42:29.040
 But they can build flying cars.

42:29.040 --> 42:33.040
 So your sense is that there's a lot of pieces that have come together.

42:33.040 --> 42:39.040
 So on this crazy question, if you look at companies like Kitty Hawk,

42:39.040 --> 42:45.040
 working on electric, so the clean, talking as the bashing through.

42:45.040 --> 42:52.040
 It's a crazy dream, but you work with flight a lot.

42:52.040 --> 42:58.040
 You've mentioned before that manned flights or carrying a human body

42:58.040 --> 43:01.040
 is very difficult to do.

43:01.040 --> 43:04.040
 So how crazy is flying cars?

43:04.040 --> 43:11.040
 Do you think there will be a day when we have vertical takeoff and landing vehicles

43:11.040 --> 43:17.040
 that are sufficiently affordable that we're going to see a huge amount of them?

43:17.040 --> 43:21.040
 And they would look like something like we dream of when we think about flying cars.

43:21.040 --> 43:23.040
 Yeah, like the Jetsons.

43:23.040 --> 43:26.040
 So look, there are a lot of smart people working on this.

43:26.040 --> 43:32.040
 And you never say something is not possible when you're people like Sebastian Thrun working on it.

43:32.040 --> 43:35.040
 So I totally think it's viable.

43:35.040 --> 43:38.040
 I question, again, the electric piece.

43:38.040 --> 43:40.040
 The electric piece, yeah.

43:40.040 --> 43:42.040
 For short distances, you can do it.

43:42.040 --> 43:46.040
 And there's no reason to suggest that these all just have to be rotor crafts.

43:46.040 --> 43:50.040
 You take off vertically, but then you morph into a forward flight.

43:50.040 --> 43:52.040
 I think there are a lot of interesting designs.

43:52.040 --> 43:56.040
 The question to me is, are these economically viable?

43:56.040 --> 44:02.040
 And if you agree to do this with fossil fuels, it instantly immediately becomes viable.

44:02.040 --> 44:04.040
 That's a real challenge.

44:04.040 --> 44:09.040
 Do you think it's possible for robots and humans to collaborate successfully on tasks?

44:09.040 --> 44:18.040
 So a lot of robotics folks that I talk to and work with, I mean, humans just add a giant mess to the picture.

44:18.040 --> 44:22.040
 So it's best to remove them from consideration when solving specific tasks.

44:22.040 --> 44:24.040
 It's very difficult to model.

44:24.040 --> 44:26.040
 There's just a source of uncertainty.

44:26.040 --> 44:36.040
 In your work with these agile flying robots, do you think there's a role for collaboration with humans?

44:36.040 --> 44:43.040
 Is it best to model tasks in a way that doesn't have a human in the picture?

44:43.040 --> 44:48.040
 I don't think we should ever think about robots without human in the picture.

44:48.040 --> 44:54.040
 Ultimately, robots are there because we want them to solve problems for humans.

44:54.040 --> 44:58.040
 But there's no general solution to this problem.

44:58.040 --> 45:02.040
 I think if you look at human interaction and how humans interact with robots,

45:02.040 --> 45:06.040
 you know, we think of these in sort of three different ways.

45:06.040 --> 45:09.040
 One is the human commanding the robot.

45:09.040 --> 45:13.040
 The second is the human collaborating with the robot.

45:13.040 --> 45:19.040
 So for example, we work on how a robot can actually pick up things with a human and carry things.

45:19.040 --> 45:21.040
 That's like true collaboration.

45:21.040 --> 45:26.040
 And third, we think about humans as bystanders, self driving cars.

45:26.040 --> 45:33.040
 What's the human's role and how do self driving cars acknowledge the presence of humans?

45:33.040 --> 45:36.040
 So I think all of these things are different scenarios.

45:36.040 --> 45:39.040
 It depends on what kind of humans, what kind of tasks.

45:39.040 --> 45:45.040
 And I think it's very difficult to say that there's a general theory that we all have for this.

45:45.040 --> 45:52.040
 But at the same time, it's also silly to say that we should think about robots independent of humans.

45:52.040 --> 45:59.040
 So to me, human robot interaction is almost a mandatory aspect of everything we do.

45:59.040 --> 46:00.040
 Yes.

46:00.040 --> 46:05.040
 But to wish to agree, so your thoughts, if we jump to autonomous vehicles, for example,

46:05.040 --> 46:10.040
 there's a big debate between what's called level two and level four.

46:10.040 --> 46:13.040
 So semi autonomous and autonomous vehicles.

46:13.040 --> 46:19.040
 And sort of the Tesla approach currently at least has a lot of collaboration between human and machine.

46:19.040 --> 46:24.040
 So the human is supposed to actively supervise the operation of the robot.

46:24.040 --> 46:33.040
 Part of the safety definition of how safe a robot is in that case is how effective is the human in monitoring it.

46:33.040 --> 46:43.040
 Do you think that's ultimately not a good approach in sort of having a human in the picture,

46:43.040 --> 46:51.040
 not as a bystander or part of the infrastructure, but really as part of what's required to make the system safe?

46:51.040 --> 46:53.040
 This is harder than it sounds.

46:53.040 --> 47:01.040
 I think, you know, if you, I mean, I'm sure you've driven before in highways and so on,

47:01.040 --> 47:10.040
 it's really very hard to relinquish controls to a machine and then take over when needed.

47:10.040 --> 47:18.040
 So I think Tesla's approach is interesting because it allows you to periodically establish some kind of contact with the car.

47:18.040 --> 47:24.040
 Toyota, on the other hand, is thinking about shared autonomy or collaborative autonomy as a paradigm.

47:24.040 --> 47:31.040
 If I may argue, these are very, very simple ways of human robot collaboration because the task is pretty boring.

47:31.040 --> 47:34.040
 You sit in a vehicle, you go from point A to point B.

47:34.040 --> 47:42.040
 I think the more interesting thing to me is, for example, search and rescue, I've got a human first responder, robot first responders.

47:42.040 --> 47:44.040
 I got to do something.

47:44.040 --> 47:45.040
 It's important.

47:45.040 --> 47:47.040
 I have to do it in two minutes.

47:47.040 --> 47:48.040
 The building is burning.

47:48.040 --> 47:50.040
 There's been an explosion.

47:50.040 --> 47:51.040
 It's collapsed.

47:51.040 --> 47:52.040
 How do I do it?

47:52.040 --> 47:58.040
 I think to me, those are the interesting things where it's very, very unstructured and what's the role of the human?

47:58.040 --> 47:59.040
 What's the role of the robot?

47:59.040 --> 48:02.040
 Clearly, there's lots of interesting challenges.

48:02.040 --> 48:05.040
 As a field, I think we're going to make a lot of progress in this area.

48:05.040 --> 48:07.040
 Yeah, it's an exciting form of collaboration.

48:07.040 --> 48:08.040
 You're right.

48:08.040 --> 48:15.040
 In the autonomous driving, the main enemy is just boredom of the human as opposed to the rescue operations.

48:15.040 --> 48:23.040
 It's literally life and death and the collaboration enables the effective completion of the mission.

48:23.040 --> 48:24.040
 So it's exciting.

48:24.040 --> 48:27.040
 Well, in some sense, we're also doing this.

48:27.040 --> 48:37.040
 You think about the human driving a car and almost invariably the human is trying to estimate the state of the car, the state of the environment, and so on.

48:37.040 --> 48:40.040
 But what is the car where to estimate the state of the human?

48:40.040 --> 48:47.040
 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.

48:47.040 --> 48:53.040
 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.

48:53.040 --> 48:59.040
 Because of some Gmail calendar entry or something like that.

48:59.040 --> 49:02.040
 And it's trying to constantly figure out who you are, what you're doing.

49:02.040 --> 49:06.040
 If a car were to do that, maybe that would make the driver safer.

49:06.040 --> 49:14.040
 Because the car is trying to figure out there's a driver paying attention, looking at his or her eyes, looking at circadian movements.

49:14.040 --> 49:16.040
 So I think the potential is there.

49:16.040 --> 49:21.040
 But from the reverse side, it's not robot modeling, but it's human modeling.

49:21.040 --> 49:23.040
 It's more in the human, right?

49:23.040 --> 49:30.040
 And I think the robots can do a very good job of modeling humans if you really think about the framework that you have.

49:30.040 --> 49:39.040
 A human sitting in a cockpit surrounded by sensors, all staring at him, in addition to be staring outside, but also staring at him.

49:39.040 --> 49:41.040
 I think there's a real synergy there.

49:41.040 --> 49:48.040
 Yeah, I love that problem because it's the new 21st century form of psychology actually, AI enabled psychology.

49:48.040 --> 49:54.040
 A lot of people have sci fi inspired fears of walking robots like those from Boston Dynamics.

49:54.040 --> 49:59.040
 If you just look at shows on Netflix and so on, or flying robots like those you work with.

49:59.040 --> 50:03.040
 How would you, how do you think about those fears?

50:03.040 --> 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 --> 50:23.040
 Any time we develop a technology meaning to have positive impact in the world, there's always a worry that somebody could subvert those technologies and use it in an adversarial setting.

50:23.040 --> 50:25.040
 And robotics is no exception, right?

50:25.040 --> 50:29.040
 So I think it's very easy to weaponize robots.

50:29.040 --> 50:31.040
 I think we talk about swarms.

50:31.040 --> 50:38.040
 One thing I worry a lot about is, for us to get swarms to work and do something reliably, it's really hard.

50:38.040 --> 50:44.040
 But suppose I have this challenge of trying to destroy something.

50:44.040 --> 50:49.040
 And I have a swarm of robots where only one out of the swarm needs to get to its destination.

50:49.040 --> 50:53.040
 So that suddenly becomes a lot more doable.

50:53.040 --> 51:00.040
 And so I worry about this general idea of using autonomy with lots and lots of agents.

51:00.040 --> 51:04.040
 I mean, having said that, look, a lot of this technology is not very mature.

51:04.040 --> 51:12.040
 My favorite saying is that if somebody had to develop this technology, wouldn't you rather the good guys do it?

51:12.040 --> 51:21.040
 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?

51:21.040 --> 51:23.040
 So we think a lot about that.

51:23.040 --> 51:28.040
 So we're doing research on how to defend against swarms, for example.

51:28.040 --> 51:29.040
 That's interesting.

51:29.040 --> 51:36.040
 There is, in fact, a report by the National Academies on counter UAS technologies.

51:36.040 --> 51:38.040
 This is a real threat.

51:38.040 --> 51:47.040
 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.

51:47.040 --> 51:49.040
 So it's not just politicians?

51:49.040 --> 51:51.040
 You think engineers have a role in this discussion?

51:51.040 --> 51:52.040
 Absolutely.

51:52.040 --> 51:59.040
 I think the days where politicians can be agnostic to technology are gone.

51:59.040 --> 52:05.040
 I think every politician needs to be literate in technology.

52:05.040 --> 52:09.040
 And I often say technology is the new liberal art.

52:09.040 --> 52:18.040
 Understanding how technology will change your life, I think, is important and every human being needs to understand that.

52:18.040 --> 52:22.040
 And maybe we can elect some engineers to office as well on the other side.

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 What are the biggest open problems in robotics in UV?

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 You said we're in the early days in some sense.

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 What are the problems we would like to solve in robotics?

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 I think there are lots of problems, right?

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 But I would phrase it in the following way.

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 If you look at the robots we're building, they're still very much tailored towards doing specific tasks in specific settings.

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 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.

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 So think of the self driving cars.

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 Today, we can build a self driving car in a parking lot.

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 We can do level five autonomy in a parking lot.

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 But can you do a level five autonomy in the streets of Napoli in Italy or Mumbai in India?

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 No.

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 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.

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 We have no understanding of how to put both those things together.

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 So we're in the very early days of applying it to the physical world.

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 And I was just in Naples, actually.

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 And there's levels of difficulty and complexity depending on which area you're applying it to.

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 I think so.

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 And we don't have a systematic way of understanding that.

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 Everybody says just because a computer can now beat a human at any board game, we suddenly know something about intelligence.

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 That's not true.

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 A computer board game is very, very structured.

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 It is the equivalent of working in a Henry Ford factory where things, parts come, you assemble, move on.

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 It's a very, very, very structured setting.

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 That's the easiest thing.

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 And we know how to do that.

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 So you've done a lot of incredible work at the UPenn University of Pennsylvania Grass Club.

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 You're now Dean of Engineering at UPenn.

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 What advice do you have for a new bright eyed undergrad interested in robotics or AI or engineering?

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 Well, I think there's really three things.

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 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?

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 Which is really hard to do.

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 So this thing about predicting the future, every one of us needs to be trying to predict the future always.

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 Not because you'll be any good at it, but by thinking about it, I think you sharpen your senses and you become smarter.

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 So that's number one.

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 Number two, it's a callery of the first piece, which is you really don't know what's going to be important.

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 So this idea that I'm going to specialize in something which will allow me to go in a particular direction.

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 It may be interesting, but it's important also to have this breadth so you have this jumping off point.

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 I think the third thing, and this is where I think Penn excels.

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 I mean, we teach engineering, but it's always in the context of the liberal arts.

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 It's always in the context of society.

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 As engineers, we cannot afford to lose sight of that.

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 So I think that's important.

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 But I think one thing that people underestimate when they do robotics is the importance of mathematical foundations,

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 the importance of representations.

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 Not everything can just be solved by looking for ROS packages on the Internet or to find a deep neural network that works.

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 I think the representation question is key, even to machine learning, where if you ever hope to achieve or get to explainable AI,

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 somehow there need to be representations that you can understand.

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 So if you want to do robotics, you should also do mathematics, and you said liberal arts, a little literature.

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 If you want to build a robot, you should be reading Dostoyevsky.

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 I agree with that.

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 Very good.

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 So Vijay, thank you so much for talking today. It was an honor.

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 Thank you. It was just a very exciting conversation. Thank you.