Brainy 'Bots

From: Ron Baalke (baalke@kelvin.jpl.nasa.gov)
Date: Tue Jun 05 2001 - 00:03:42 MET DST

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    http://science.nasa.gov/headlines/y2001/ast29may_1.htm

    Brainy 'Bots
    NASA Science News

    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."

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