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  • 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.

  • The reason that they're so popular these days

  • is 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.

  • (Applause)

  • 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.

  • 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 of water is there.

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

  • where I want it to be in space.

  • (Applause)

  • 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 as 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.

  • (Applause)

  • 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?

  • 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.

  • (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?

  • (Applause)

  • 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)

  • 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)