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	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?

	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 have limited autonomy,

	15:31.800 --> 15:34.760
	which is the ability to go from point A to point B,

	15:34.760 --> 15:38.320
	and even the more sophisticated vehicles

	15:38.320 --> 15:41.760
	can do autonomous takeoff and landing.

	15:41.760 --> 15:44.400
	And those usually have wings and they're heavy?

	15:44.400 --> 15:45.360
	Usually they're wings,

	15:45.360 --> 15:47.440
	but there's nothing preventing us from doing this

	15:47.440 --> 15:49.000
	for helicopters as well.

	15:49.000 --> 15:53.440
	There are many military organizations that have

	15:53.440 --> 15:56.560
	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 --> 16:02.800
	and it's actually very similar.

	16:02.800 --> 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 --> 16:16.880
	would they have happened if the plane

	16:16.880 --> 16:20.200
	were truly autonomous, and I think you'll find

	16:20.200 --> 16:21.960
	that in many of the cases,

	16:21.960 --> 16:24.600
	because of pilot error, we made silly decisions.

	16:24.600 --> 16:26.920
	And so in some sense, even in air traffic,

	16:26.920 --> 16:29.760
	commercial air traffic, there's a lot of applications,

	16:29.760 --> 16:33.920
	although we only see autonomy being enabled

	16:33.920 --> 16:38.920
	at very high altitudes when the plane is an autopilot.

	16:41.160 --> 16:42.520
	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.

	52:22.040 --> 52:25.040
	What are the biggest open problems in robotics in UV?

	52:25.040 --> 52:27.040
	You said we're in the early days in some sense.

	52:27.040 --> 52:30.040
	What are the problems we would like to solve in robotics?

	52:30.040 --> 52:32.040
	I think there are lots of problems, right?

	52:32.040 --> 52:36.040
	But I would phrase it in the following way.

	52:36.040 --> 52:46.040
	If you look at the robots we're building, they're still very much tailored towards doing specific tasks in specific settings.

	52:46.040 --> 52:59.040
	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.

	52:59.040 --> 53:02.040
	So think of the self driving cars.

	53:02.040 --> 53:05.040
	Today, we can build a self driving car in a parking lot.

	53:05.040 --> 53:09.040
	We can do level five autonomy in a parking lot.

	53:09.040 --> 53:17.040
	But can you do a level five autonomy in the streets of Napoli in Italy or Mumbai in India?

	53:17.040 --> 53:18.040
	No.

	53:18.040 --> 53:27.040
	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.

	53:27.040 --> 53:32.040
	We have no understanding of how to put both those things together.

	53:32.040 --> 53:36.040
	So we're in the very early days of applying it to the physical world.

	53:36.040 --> 53:39.040
	And I was just in Naples, actually.

	53:39.040 --> 53:46.040
	And there's levels of difficulty and complexity depending on which area you're applying it to.

	53:46.040 --> 53:47.040
	I think so.

	53:47.040 --> 53:51.040
	And we don't have a systematic way of understanding that.

	53:51.040 --> 54:00.040
	Everybody says just because a computer can now beat a human at any board game, we suddenly know something about intelligence.

	54:00.040 --> 54:01.040
	That's not true.

	54:01.040 --> 54:04.040
	A computer board game is very, very structured.

	54:04.040 --> 54:11.040
	It is the equivalent of working in a Henry Ford factory where things, parts come, you assemble, move on.

	54:11.040 --> 54:14.040
	It's a very, very, very structured setting.

	54:14.040 --> 54:15.040
	That's the easiest thing.

	54:15.040 --> 54:18.040
	And we know how to do that.

	54:18.040 --> 54:23.040
	So you've done a lot of incredible work at the UPenn University of Pennsylvania Grass Club.

	54:23.040 --> 54:26.040
	You're now Dean of Engineering at UPenn.

	54:26.040 --> 54:34.040
	What advice do you have for a new bright eyed undergrad interested in robotics or AI or engineering?

	54:34.040 --> 54:37.040
	Well, I think there's really three things.

	54:37.040 --> 54:45.040
	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?

	54:45.040 --> 54:46.040
	Which is really hard to do.

	54:46.040 --> 54:53.040
	So this thing about predicting the future, every one of us needs to be trying to predict the future always.

	54:53.040 --> 55:01.040
	Not because you'll be any good at it, but by thinking about it, I think you sharpen your senses and you become smarter.

	55:01.040 --> 55:02.040
	So that's number one.

	55:02.040 --> 55:09.040
	Number two, it's a callery of the first piece, which is you really don't know what's going to be important.

	55:09.040 --> 55:15.040
	So this idea that I'm going to specialize in something which will allow me to go in a particular direction.

	55:15.040 --> 55:22.040
	It may be interesting, but it's important also to have this breadth so you have this jumping off point.

	55:22.040 --> 55:25.040
	I think the third thing, and this is where I think Penn excels.

	55:25.040 --> 55:30.040
	I mean, we teach engineering, but it's always in the context of the liberal arts.

	55:30.040 --> 55:32.040
	It's always in the context of society.

	55:32.040 --> 55:35.040
	As engineers, we cannot afford to lose sight of that.

	55:35.040 --> 55:37.040
	So I think that's important.

	55:37.040 --> 55:43.040
	But I think one thing that people underestimate when they do robotics is the importance of mathematical foundations,

	55:43.040 --> 55:47.040
	the importance of representations.

	55:47.040 --> 55:56.040
	Not everything can just be solved by looking for ROS packages on the Internet or to find a deep neural network that works.

	55:56.040 --> 56:06.040
	I think the representation question is key, even to machine learning, where if you ever hope to achieve or get to explainable AI,

	56:06.040 --> 56:09.040
	somehow there need to be representations that you can understand.

	56:09.040 --> 56:16.040
	So if you want to do robotics, you should also do mathematics, and you said liberal arts, a little literature.

	56:16.040 --> 56:19.040
	If you want to build a robot, you should be reading Dostoyevsky.

	56:19.040 --> 56:20.040
	I agree with that.

	56:20.040 --> 56:21.040
	Very good.

	56:21.040 --> 56:24.040
	So Vijay, thank you so much for talking today. It was an honor.

	56:24.040 --> 56:47.040
	Thank you. It was just a very exciting conversation. Thank you.