The astounding athletic power of quadcopters

In a robot lab at TEDGlobal, Raffaelo D'Andrea demos his flying quadcopters: robots that think like athletes, solving physical problems with algorithms that help them learn. In a series of nifty demos, D'Andrea shows drones that play catch, balance and make decisions together -- and watch out for an I-want-this-now demo of Kinect-controlled quads.

So what does it mean
for a machine to be athletic?

We will demonstrate the concept
of machine athleticism

and the research to achieve it

with the help of these flying machines
called quadrocopters,

or quads, for short.

Quads have been around for a long time.

They're so popular these days
because they're mechanically simple.

By controlling
the speeds of these four propellers,

these machines can roll, pitch, yaw,

and accelerate
along their common orientation.

On board are also a battery, a computer,

various sensors and wireless radios.

Quads are extremely agile,
but this agility comes at a cost.

They are inherently unstable,

and they need some form
of automatic feedback control

in order to be able to fly.

So, how did it just do that?

Cameras on the ceiling and a laptop

serve as an indoor
global positioning system.

It's used to locate objects in the space

that have these reflective
markers on them.

This data is then sent to another laptop

that is running estimation
and control algorithms,

which in turn sends commands to the quad,

which is also running estimation
and control algorithms.

The bulk of our research is algorithms.

It's the magic that brings
these machines to life.

So how does one design the algorithms
that create a machine athlete?

We use something broadly
called model-based design.

We first capture the physics

with a mathematical model
of how the machines behave.

We then use a branch of mathematics
called control theory

to analyze these models

and also to synthesize
algorithms for controlling them.

For example, that's how we can
make the quad hover.

We first captured the dynamics
with a set of differential equations.

We then manipulate these equations
with the help of control theory

to create algorithms
that stabilize the quad.

Let me demonstrate
the strength of this approach.

Suppose that we want
this quad to not only hover

but to also balance this pole.

With a little bit of practice,

it's pretty straightforward
for a human being to do this,

although we do have the advantage
of having two feet on the ground

and the use of our very versatile hands.

It becomes a little bit more difficult

when I only have one foot on the ground

and when I don't use my hands.

Notice how this pole has
a reflective marker on top,

which means that it can
be located in the space.

(Audience) Oh!

(Applause)

(Applause ends)

You can notice that this quad
is making fine adjustments

to keep the pole balanced.

How did we design
the algorithms to do this?

We added the mathematical
model of the pole

to that of the quad.

Once we have a model
of the combined quad-pole system,

we can use control theory to create
algorithms for controlling it.

Here, you see that it's stable,

and even if I give it little nudges,

it goes back --

to the nice, balanced position.

We can also augment the model

to include where we want
the quad to be in space.

Using this pointer,
made out of reflective markers,

I can point to where I want
the quad to be in space

a fixed distance away from me.

(Laughter)

The key to these acrobatic
maneuvers is algorithms,

designed with the help
of mathematical models

and control theory.

Let's tell the quad to come back here

and let the pole drop,

and I will next demonstrate the importance

of understanding physical models

and the workings of the physical world.

Notice how the quad lost altitude
when I put this glass of water on it.

Unlike the balancing pole,

I did not include the mathematical
model of the glass

in the system.

In fact, the system doesn't even know
that the glass is there.

Like before, I could use
the pointer to tell the quad

where I want it to be in space.

(Applause)

(Applause ends)

Okay, you should be asking yourself,

why doesn't the water
fall out of the glass?

Two facts.

The first is that gravity acts
on all objects in the same way.

The second is that the propellers

are all pointing in the same direction
of the glass, pointing up.

You put these two things together,

the net result is that all side forces
on the glass are small

and are mainly dominated
by aerodynamic effects,

which at these speeds are negligible.

And that's why you don't need
to model the glass.

It naturally doesn't spill,
no matter what the quad does.

(Audience) Oh!

(Applause)

(Applause ends)

The lesson here

is that some high-performance tasks
are easier than others,

and that understanding
the physics of the problem

tells you which ones are easy
and which ones are hard.

In this instance, carrying
a glass of water is easy.

Balancing a pole is hard.

We've all heard stories of athletes
performing feats while physically injured.

Can a machine also perform
with extreme physical damage?

Conventional wisdom says

that you need at least four fixed motor
propeller pairs in order to fly,

because there are four degrees
of freedom to control:

roll, pitch, yaw and acceleration.

Hexacopters and octocopters,
with six and eight propellers,

can provide redundancy,

but quadrocopters are much more popular

because they have the minimum number
of fixed motor propeller pairs: four.

Or do they?

(Audience) Oh!

(Laughter)

If we analyze the mathematical
model of this machine

with only two working propellers,

we discover that there's
an unconventional way to fly it.

We relinquish control of yaw,

but roll, pitch and acceleration
can still be controlled

with algorithms that exploit
this new configuration.

Mathematical models tell us
exactly when and why this is possible.

In this instance, this knowledge
allows us to design

novel machine architectures

or to design clever algorithms
that gracefully handle damage,

just like human athletes do,

instead of building
machines with redundancy.

We can't help but hold our breath

when we watch a diver
somersaulting into the water,

or when a vaulter is twisting in the air,

the ground fast approaching.

Will the diver be able
to pull off a rip entry?

Will the vaulter stick the landing?

Suppose we want this quad here
to perform a triple flip

and finish off at the exact same
spot that it started.

This maneuver is going
to happen so quickly

that we can't use position feedback
to correct the motion during execution.

There simply isn't enough time.

Instead, what the quad can do
is perform the maneuver blindly,

observe how it finishes the maneuver,

and then use that information
to modify its behavior

so that the next flip is better.

Similar to the diver and the vaulter,

it is only through repeated practice

that the maneuver can
be learned and executed

to the highest standard.

(Laughter)

(Applause)

Striking a moving ball
is a necessary skill in many sports.

How do we make a machine do

what an athlete does
seemingly without effort?

(Laughter)

(Applause)

(Applause ends)

This quad has a racket
strapped onto its head

with a sweet spot roughly the size
of an apple, so not too large.

The following calculations
are made every 20 milliseconds,

or 50 times per second.

We first figure out where
the ball is going.

We then next calculate
how the quad should hit the ball

so that it flies
to where it was thrown from.

Third, a trajectory is planned
that carries the quad

from its current state
to the impact point with the ball.

Fourth, we only execute 20 milliseconds'
worth of that strategy.

Twenty milliseconds later,
the whole process is repeated

until the quad strikes the ball.

(Applause)

Machines can not only perform
dynamic maneuvers on their own,

they can do it collectively.

These three quads are cooperatively
carrying a sky net.

(Applause)

(Applause ends)

They perform an extremely dynamic
and collective maneuver

to launch the ball back to me.

Notice that, at full extension,
these quads are vertical.

(Applause)

In fact, when fully extended,

this is roughly five times greater
than what a bungee jumper feels

at the end of their launch.

The algorithms to do this are very similar

to what the single quad used
to hit the ball back to me.

Mathematical models are used
to continuously re-plan

a cooperative strategy
50 times per second.

Everything we have seen so far has been
about the machines and their capabilities.

What happens when we couple
this machine athleticism

with that of a human being?

What I have in front of me
is a commercial gesture sensor

mainly used in gaming.

It can recognize
what my various body parts

are doing in real time.

Similar to the pointer
that I used earlier,

we can use this as inputs to the system.

We now have a natural way of interacting

with the raw athleticism
of these quads with my gestures.

(Applause)

Interaction doesn't have to be virtual.

It can be physical.

Take this quad, for example.

It's trying to stay
at a fixed point in space.

If I try to move it
out of the way, it fights me,

and moves back to where it wants to be.

We can change this behavior, however.

We can use mathematical models

to estimate the force
that I'm applying to the quad.

Once we know this force,
we can also change the laws of physics,

as far as the quad
is concerned, of course.

Here, the quad is behaving
as if it were in a viscous fluid.

We now have an intimate way
of interacting with a machine.

I will use this new capability to position

this camera-carrying quad
to the appropriate location

for filming the remainder
of this demonstration.

So we can physically interact
with these quads

and we can change the laws of physics.

Let's have a little bit of fun with this.

For what you will see next,

these quads will initially behave
as if they were on Pluto.

As time goes on, gravity will be increased

until we're all back on planet Earth,

but I assure you we won't get there.

Okay, here goes.

(Laughter)

(Laughter)

(Applause)

Whew!

You're all thinking now,

these guys are having way too much fun,

and you're probably also asking yourself,

why exactly are they building
machine athletes?

Some conjecture that the role
of play in the animal kingdom

is to hone skills
and develop capabilities.

Others think that it has
more of a social role,

that it's used to bind the group.

Similarly, we use the analogy
of sports and athleticism

to create new algorithms for machines

to push them to their limits.

What impact will the speed
of machines have on our way of life?

Like all our past creations
and innovations,

they may be used to improve
the human condition

or they may be misused and abused.

This is not a technical choice
we are faced with;

it's a social one.

Let's make the right choice,

the choice that brings out the best
in the future of machines,

just like athleticism in sports
can bring out the best in us.

Let me introduce you to the wizards
behind the green curtain.

They're the current members
of the Flying Machine Arena research team.

(Applause)

Federico Augugliaro, Dario Brescianini,

Markus Hehn, Sergei Lupashin,
Mark Muller and Robin Ritz.

Look out for them.
They're destined for great things.

Thank you.

(Applause)