NASA’s own “Bionic Woman” is applying artificial intelligence to teach
robots how to behave a little more like human explorers.
May 29, 2001 — Ayanna Howard may never set foot on Mars or lead a mission
to Jupiter, but the work she’s doing on “smart” robots will help to
revolutionize planetary exploration nonetheless.
As a project scientist specializing in artificial intelligence at NASA’s Jet
Propulsion Laboratory (JPL), Ayanna is part of a team that applies creative
energy to a new generation of space missions — planetary and moon surface
explorations led by autonomous robots capable of “thinking” for themselves.
Nearly all of today’s robotic space probes are inflexible in how they
respond to the challenges they encounter (one notable exception is Deep
Space 1, which employs artificial intelligence technologies). They can only
perform actions that are explicitly written into their software or radioed
from a human controller on Earth.
When exploring unfamiliar planets millions of miles from Earth, this
“obedient dog” variety of robot requires constant attention from humans. In
contrast, the ultimate goal for Ayanna and her colleagues is “putting a
robot on Mars and walking away, leaving it to work without direct human
interaction.”
“We want to tell the robot to think about any obstacle it encounters just as
an astronaut in the same situation would do,” she says. “Our job is to help
the robot think in more logical terms about turning left or right, not just
by how many degrees.”
How could a robot possibly make decisions like a human?
Scientists are developing suitable techniques by learning from humans’
vision and observation abilities.
Humans don’t have a rulebook or program to consult for each move they make,
Ayanna notes — we’re much more reactive than that. Her team’s job is to
produce robots that can emulate not only the thought process and judgment of
a human for sizing up the terrain, but also a human’s ability to drive and
navigate a car in real time.
Above: Ayanna Howard has a doctorate in electrical engineering from the
University of Southern California, specializing in artificial intelligence
and robotics. She has worked at JPL since 1993.
To do this, Ayanna and her colleagues rely on two concepts in the field of
artificial intelligence: “fuzzy logic” and “neural networks.”
Fuzzy logic allows computers to operate not only in terms of black and white
— true or false — but also in shades of gray. For example, a traditional
computer would take the height measurement of a tree and assign that tree to
some category — say, “tall.” But a fuzzy logic computer would say the tree
has a 78 percent chance (for example) of belonging to the category “tall”
and a 22 percent chance of belonging to some other category. The sharp
distinction between “tall” and “short” becomes fuzzy.
This probabilistic approach to categorization allows the computer to learn
from its experiences, since the assigning of probabilities can be adjusted
the next time a similar object is encountered. Fuzzy logic is already in use
today in software such as computer speech and handwriting recognition
programs, which learn to perform better through “training.”
Neural networks also have the ability to learn from experience. This
shouldn’t be too surprising, since the design of neural networks mimics the
way brain cells — called “neurons” — process information.
“Neural networks allow you to associate general input to a specific output,”
Ayanna says. “When someone sees four legs and hears a bark (the input),
their experience lets them know it is a dog (the output).” This feature of
neural networks will allow a robot pioneer to choose behaviors based on the
general features of its surroundings, much like humans do.
To accomplish this, neural nets contain several layers of “nodes,” which are
analogous to neurons. Each node in one layer is connected to nodes in the
other layers. Signals travel through this web of connections with each node
acting as a gate, only relaying signals above a certain strength. Adjusting
that threshold for individual nodes is how the network “learns.”
This dinner-napkin sketch of neural nets may sound relatively simple, but in
practice, these artificial brains can perform some astoundingly complex
logic. In fact, Ayanna calls neural nets a “black-box technology” — in
other words, what happens between the input layer and the output layer is
often so difficult to decipher that scientists just treat it as a “black
box” that somehow converts inputs into outputs.
By combining these two technologies, Ayanna and her colleagues at JPL hope
to create a robot “brain” that can learn on its own how to expertly traverse
the alien terrains of other planets.
Such a brainy ‘bot might sound more like the science fiction fantasies of
children’s comics than a real NASA project, but Ayanna thinks the sci-fi
flavor of the project contributes to its importance for space exploration.
Ayanna — who wanted to be television’s “Bionic Woman” when she was young,
and later decided she wanted to try to build her instead — says she
believes that the flights of imagination common in childhood translate into
adult scientific achievement.
“I truly believe science fiction drives real science forward,” she says.
“You must have imagination to go to the next level.”
