Innovators Under 35
INNOVATIVE Pioneers
P7. Angela Schoellig, 34
University
of Toronto
Her algorithms are helping
self-driving and self-flying vehicles get around more safely.
Safety
never used to be much of a concern with machine-learning systems. Any goof made
in image labeling or speech recognition might be annoying, but it wouldn’t put
anybody’s life at risk. But autonomous cars, drones, and manufacturing robots
have raised the stakes.
Angela
Schoellig, who leads the Dynamic Systems Lab at the University of Toronto, has
developed learning algorithms that allow robots to learn together and from each
other in order to ensure that, for example, a flying robot never crashes into a
wall while navigating an unknown place, or that a self-driving vehicle never
leaves its lane when driving in a new city. Her work has demonstrably extended
the capabilities of today’s robots, enabling self-flying and self-driving
vehicles to fly or drive along a predefined path despite uncertainties such as
wind, changing payloads, or unknown road conditions.
As
a PhD student at the Swiss Federal Institute of Technology in Zurich, Schoellig
worked with others to develop the Flying Machine Arena, a 10-cubic-meter space
for training drones to fly together in an enclosed area. In 2010, she created a
performance in which a fleet of UAVs flew synchronously to music. The “dancing
quadrocopter” project, as it became known, used algorithms that allowed the
drones to adapt their movements to match the music’s tempo and character and
coordinate to avoid collision, without the need for researchers to manually
control their flight paths. Her setup decoupled two essential, usually
intertwined components of autonomous systems—perception and action—by placing,
at the center of the space, a high-precision overhead motion-capture system
that can perfectly locate multiple objects at rates exceeding 200 frames per
second. This external system enabled the team to concentrate resources on the
vehicle-control algorithms.
—Simon Parkin
MIT TECHNOLOGY REVIEW
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