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LIAM McHUGH: The speed of Ted Ligety.
TED LIGETY: You have to be pretty dialed in on
where you're going.
McHUGH: The power of Julie Chu.
JULIE CHU: The physical part of the game is something
that we embrace.
McHUGH: The artistry of Meryl Davis and Charlie White.
MERYL DAVIS: There's a lot of complexity involved in making
each element as efficient as possible.
McHUGH: Every four years the Olympic winter games are a
showcase of human movement and athletic achievement, with
performances so dazzling they have the ability to
amaze and inspire.
RAFFAELLO D'ANDREA: Athletes push the boundaries of human
abilities, well; we want to push the boundaries in a similar way
with our research.
McHUGH: Raffaello D'Andrea is a professor of dynamic systems and
control at the Swiss Federal Institute of Technology in
Zurich, Switzerland. He was supported by the National
Science Foundation while on the faculty at Cornell University in
Ithaca, New York.
He and his colleagues at ETH Zurich are at the forefront
of robotic engineering, working with machines that mimic human
movement, such as the small flying vehicle known as the
quadrocopter, or quad. It's similar to a helicopter but with
four independent rotors that allow it to move with great
agility and acrobatic ability.
D'ANDREA: A lot of times what we end up doing looks very similar
to what human beings would do when playing sports.
McHUGH: But what makes the quad so innovative isn't just the
way it moves, it's the machine's ability to learn and
improve its performance.
D'ANDREA: It's similar to what athletes do when they practice.
They do the same task over and over again. And by doing it they
can get better over time. Our machines do the same thing.
McHUGH: To showcase the quad's abilities, D'Andrea's team sets
it up to complete a series of experiments that are similar to
how Olympic athletes move. They begin with a slalom course to
teach the quad how to maneuver through a series of poles like
skier Ted Ligety.
MAN #1: Are you ready?
MAN #2: Yep. MAN #1: Ok, let's start it!
McHUGH: Using mathematical models to roughly tell them how
the quad should control itself, D'Andrea's team writes
algorithms, or a sequence of instructions that help the quad
figure out how to execute its task.
D'ANDREA: The process for us when designing a robot is we
really think of a task first. And then we figure out, you
know, what kind of design does the robot have to have in order
to do that?
McHUGH: The test is carried out in a special arena equipped with
cameras that are part of the quad's control systems. These
systems measure the quad's physical progress, and provide
feedback that helps the quad make adjustments during
further test runs.
D'ANDREA: And over a period of time, five or six iterations,
what it couldn't do at - in the very first trial, it can do
now quite well.
McHUGH: This method of machine learning also applies if the
quad wants to intercept and bounce a ball back to where it
was thrown, much like hockey player Julie Chu passing the
puck to a teammate.
D'ANDREA: They first have to put the puck in an open space, but
knowing exactly where the other player will come by and hit the
puck perhaps to another player. Our vehicles are doing the same
thing. They are seeing where the ball is flying and then they're
executing algorithms that figure out where should I intercept the
ball so that when I hit it, it goes exactly where I want
it to go.
McHUGH: In ice dancing, Meryl Davis and Charlie White must
react in real time to each other, and make quick
adjustments to produce a near flawless performance.
D'ANDREA: The quadrocopters are similar in that way. But at the
same time, they must react to changes in the conditions if one
of their partners doesn't do what they're supposed
to be doing.
McHUGH: In this scenario, the researchers create a
mathematical model of a quad combined with an object it needs
to balance. Then, with feedback from its control systems,
algorithms are built so the quad can complete its task.
D'ANDREA: If you find that the machine needs improvement,
you're going to redesign it, you do more simulation; develop new
algorithms and the process builds up until you have a
machine that does incredible feats.
McHUGH: People often marvel at how effortlessly athletes and
the quad perform their tasks. Both robot and Olympian have
something in common: they need to practice their moves
countless times before it looks easy.
D'ANDREA: It's very motivating to watch professional athletes
and world class athletes at the top of their game. It certainly
motivates us to do similar things with our machines.
McHUGH: Whether it's skiing, hockey, ice dancing or other
sports, the athletes of the 2014 Olympic Winter Games will serve
as a source of inspiration for D'Andrea and his team. They in
turn hope their research will help lay the groundwork for the
design of more responsive and agile robotic devices.
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