Popular culture has long pondered the question, “If it looks like a
human, walks like a human and talks like a human, is it human?” So far
the answer has been no. Robots can’t cry, bleed or feel like humans,
and that’s part of what makes them different. But what if they could
think like humans?

Biologically inspired robots aren’t just an ongoing fascination in
movies and comic books; they are being realized by engineers and
scientists all over the world. While much emphasis is placed on
developing physical characteristics for robots, like functioning
human-like faces or artificial muscles, engineers in the Telerobotics
Research and Applications Group at NASA’s Jet Propulsion Laboratory,
Pasadena, Calif., are among those working to program robots with forms
of artificial intelligence similar to human thinking processes.

Why Would They Want to Do That?

“The way robots function now, if something goes wrong, humans modify
their programming code and reload everything, then hope it eventually
works,” said JPL robotics engineer Barry Werger. “What we hope to do
eventually is get robots to be more independent and learn to adjust
their own programming.”

Scientists and engineers take several approaches to control robots.
The two extreme ends of the spectrum are called “deliberative control”
and “reactive control.” The former is the traditional, dominant way in
which robots function, by painstakingly constructing maps and other
types of models that they use to plan sequences of action with
mathematical precision. The robot performs these sequences like a
blindfolded pirate looking for buried treasure; from point A, move 36
paces north, then 12 paces east, then 4 paces northeast to point X;
thar be the gold.

The downside to this is that if anything interrupts the robot’s
progress (for example, if the map is wrong or lacks detail), the robot
must stop, make a new map and a new plan of actions. This re-planning
process can become costly if repeated over time. Also, to ensure the
robot’s safety, back-up programs must be in place to abort the plan if
the robot encounters an unforeseen rock or hole that may hinder its

“Reactive” approaches, on the other hand, get rid of maps and planning
altogether and focus on live observation of the environment. Slow down
if there’s a rock ahead. Dig if you see a big X on the ground.

The JPL Telerobotics Research and Applications Group, led by technical
group supervisor Dr. Homayoun Seraji, focuses on “behavior-based
control,” which lies toward the “reactive” end of the spectrum.
Behavior-based control allows robots to follow a plan while staying
aware of the unexpected, changing features of their environment. Turn
right when you see a red rock, go all the way down the hill and dig
right next to the palm tree; thar be the gold.

Behavior-based control allows the robot a great deal of flexibility to
adapt the plan to its environment as it goes, much as a human does.
This presents a number of advantages in space exploration, including
alleviating the communication delay that results from operating
distant rovers from Earth.

How Do They Do It?

Seraji’s group at JPL focuses on two of the many approaches to
implementing behavior-based control: fuzzy logic and neural networks.
The main difference between the two systems is that robots using fuzzy
logic perform with a set knowledge that doesn’t improve; whereas,
robots with neural networks start out with no knowledge and learn over

Fuzzy Logic

“Fuzzy logic rules are a way of expressing actions as a human would,
with linguistic instead of mathematical commands; for example, when
one person says to another person, ‘It’s hot in here,’ the other
person knows to either open the window or turn up the air
conditioning. That person wasn’t told to open the window, but he or
she knew a rule such as ‘when it is hot, do something to stay cool,'”
said Seraji, a leading expert in robotic control systems who was
recently recognized as the most published author in the Journal of
Robotic Systems’ 20-year history.

By incorporating fuzzy logic into their engineering technology, robots
can function in a humanistic way and respond to visual or audible
signals, or in the case of the above example, turn on the air
conditioning when it thinks the room is hot.

Neural Networks

Neural networks are tools that allow robots to learn from their
experiences, associate perceptions with actions and adapt to
unforeseen situations or environments.

“The concepts of ‘interesting’ and ‘rocky’ are ambiguous in nature,
but can be learned using neural networks,” said JPL robotics research
engineer Dr. Ayanna Howard, who specializes in artificial intelligence
and creates intelligent technology for space applications. “We can
train a robot to know that if it encounters rocky surfaces, then the
terrain is hazardous. Or if the rocky surface has interesting
features, then it may have great scientific value.”

Neural networks mimic the human brain in that they simulate a large
network of simple elements, similar to brain cells, that learn through
being presented with examples. A robot functioning with such a system
learns somewhat like a baby or a child does, only at a slower rate.

“We can easily tell a robot that a square is an equilateral object
with four sides, but how do we describe a cat?” Werger said. “With
neural networks, we can show the robot many examples of cats, and it
will later be able to recognize cats in general.”

Similarly, a neural network can ‘learn’ to classify terrain if a
geologist shows it images of many types of terrain and associates a
label with each one. When the network later sees an image of a terrain
it hasn’t seen before, it can determine whether the terrain is
hazardous or safe based on its lessons.

Robotics for Today and Tomorrow

With continuous advances in robotic methods like behavior-based
control, future space missions might be able to function without
relying heavily on human commands. On the home front, similar
technology is already used in many practical applications such as
digital cameras, computer programs, dishwashers, washing machines and
some car engines. The post office even uses neural networks to read
handwriting and sort mail.

“Does this mean robots in the near future will think like humans? No,”
Werger said. “But by mimicking human techniques, they could become
easier to communicate with, more independent, and ultimately more

JPL is a division of the California Institute of Technology in
Pasadena, Calif.