The Universal Robot
© July 1991 by Hans Moravec
Robotics Institute
Carnegie Mellon University
Pittsburgh, PA 15213
USA
(412) 268-3829
(4700 words)
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Abstract
Our artefacts are getting smarter, and a loose parallel with
the evolution of animal intelligence suggests one future course for
them. Computerless industrial machinery exhibits the behavioral
flexibility of single-celled organisms. Today's best
computer-controlled robots are like the simpler invertebrates. A
thousand-fold increase in computer power in the next decade should
make possible machines with reptile-like sensory and motor competence.
Properly configured, such robots could do in the physical world what
personal computers now do in the world of data—act on our behalf
as literal-minded slaves. Growing computer power over the next
half-century will allow this reptile stage will be surpassed, in
stages producing robots that learn like mammals, model their world
like primates and eventually reason like humans. Depending on your
point of view, humanity will then have produced a worthy successor, or
shaken off some of its inherited limitations and so transformed itself
into something quite new.
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Instincts which predispose the nature and quantity of work we enjoy
probably evolved during the 100,000 years our ancestors lived as
hunter-gatherers. Less than 10,000 years ago the agricultural
revolution made life more stable, and richer in goods and information.
But, paradoxically, it requires more human labor to support an
agricultural society than a primitive one, and the work is of a
different, “unnatural” kind, out of step with the old
instincts. The effort to avoid this work has resulted in
domestication of animals, slavery and the industrial revolution. But
many jobs must still be done by hand, engendering for hundreds of
years the fantasy of an intelligent but soulless being that can
tirelessly dispatch the drudgery. Only in this century have
electronic sensors and computers given machines the ability to sense
their world and to think about it, and so offered a way to fulfil the
wish. As in fables, the unexpected side effects of robot slaves are
likely to dominate the resulting story. Most significantly, these
perfect slaves will continue to develop, and will not long remain
soulless. As they increase in competence they will have occasion to
make more and more autonomous decisions, and so will slowly develop a
volition and purposes of their own. At the same time they will become
indispensable. Our minds were evolved to store the skills and
memories of a stone-age life, not the enormous complexity that has
developed in the last ten thousand years. We've kept up, after a
fashion, through a series of social inventions—social
stratification and division of labor, memory aids like poetry and
schooling, written records stored outside the body, and recently
machines that can do some of our thinking entirely without us. The
portion of absolutely essential human activity that takes place
outside of human bodies and minds has been steadily increasing. Hard
working intelligent machines may complete the trend.
Serious attempts to build thinking machines began after the second
world war. One line of research, called Cybernetics, used simple
electronic circuitry to mimic small nervous systems, and produced
machines that could learn to recognize simple patterns, and
turtle-like robots that found their way to lighted recharging hutches
[Wiener61]. An entirely different approach, named Artificial
Intelligence (AI), attempted to duplicate rational human thought in
the large computers that appeared after the war. By 1965, these
computers ran programs that proved theorems in logic and geometry,
solved calculus problems and played good games of checkers
[Feigenbaum63]. In the early 1970s, AI research groups at MIT (the
Massachusetts Institute of Technology) and Stanford University
attached television cameras and robot arms to their computers, so
their “thinking” programs could begin to collect their
information directly from the real world.
What a shock! While the pure reasoning programs did their jobs about
as well and about as fast as college freshmen, the best robot control
programs took hours to find and pick up a few blocks on a table.
Often these robots failed completely, giving a performance much worse
than a six month old child. This disparity between programs that
reason and programs that perceive and act in the real world holds to
this day. In recent years Carnegie Mellon University produced two
desk-sized computers that can play chess at grandmaster level, within
the top 100 players in the world, when given their moves on a
keyboard. But present-day robotics could produce only a complex and
unreliable machine for finding and moving normal chess pieces.
In hindsight it seems that, in an absolute sense, reasoning is much
easier than perceiving and acting—a position not hard to
rationalize in evolutionary terms. The survival of human beings (and
their ancestors) has depended for hundreds of millions of years on
seeing and moving in the physical world, and in that competition large
parts of their brains have become efficiently organized for the task.
But we didn't appreciate this monumental skill because it is shared by
every human being and most animals—it is commonplace. On the
other hand, rational thinking, as in chess, is a newly acquired skill,
perhaps less than one hundred thousand years old. The parts of our
brain devoted to it are not well organized, and, in an absolute sense,
we're not very good at it. But until recently we had no competition
to show us up.
By comparing the edge and motion detecting circuitry in the four
layers of nerve cells in the retina, the best understood major circuit
in the human nervous system, with similar processes developed for
“computer vision” systems that allow robots in research and
industry to see, I've estimated that it would take a billion
computations per second (the power of a world-leading Cray 2
supercomputer) to produce the same results at the same speed as a
human retina. By extrapolation, to emulate a whole brain takes ten
trillion arithmetic operations per second, or ten thousand Crays worth
[Moravec88]. This is for operations our nervous system do extremely
efficiently and well. Arithmetic provides an example at the other
extreme. In 1989 a new computer was tested for a few months with a
program that computed the number p to more than
one billion decimal places. By contrast, the largest unaided manual
computation of p was 707 digits by William Shanks
in 1873. It took him several years, and because of a mistake every
digit past the 527th was wrong! In arithmetic, today's average
computers are one million times more powerful than human beings. In
very narrow areas of rational thought (like playing chess or
proving theorems) they are about the same. And in perception and
control of movement in the complex real world, and related areas of
common-sense knowledge and intuitive and visual problem solving,
today's average computers are a million times less capable. The
deficit is evident even in pure problem solving AI programs. To this
day AI programs exhibit no shred of common sense—a medical
diagnosis program, for instance, may prescribe an antibiotic when
presented a broken bicycle because it lacks a model of people,
diseases or bicycles. Yet these programs, on existing computers,
would be overwhelmed were they to be bloated with the details of
everyday life, since each new fact can interact with the others in an
astronomical “combinatorial explosion.” [A ten year project
called Cyc at the Microelectronics and Computer Consortium in
Austin Texas is attempting to build just such a common-sense data
base. They estimate the final result will contain over one hundred
million logic sentences about everyday objects and actions [Lenat89].]
Machines have a lot of catching up to do. On the other hand, for
most of the century, machine calculation has been improving a
thousandfold every twenty years, and there are basic developments in
research labs that can sustain this for at least several decades more.
In less than fifty years computer hardware should be powerful enough
to match, and exceed, even the well-developed parts of human
intelligence. But what about the software that would be required to
give these powerful machines the ability to perceive, intuit and think
as well as humans? The Cybernetic approach that attempts to directly
imitate nervous systems is very slow, partly because examining a
working brain in detail is a very tedious process. New instruments
may change that in future. The AI approach has successfully imitated
some aspects of rational thought, but that seems to be only about one
millionth of the problem. I feel that the fastest progress on the
hardest problems will come from a third approach, the newer field of
robotics, the construction of systems that must see and move in the
physical world. Robotics research is imitating the evolution
of animal minds, adding capabilities to machines a few at a time, so
that the resulting sequence of machine behaviors resembles the
capabilities of animals with increasingly complex nervous systems.
This effort to build intelligence from the bottom up is helped by
biological peeks at the “back of the book”—at the
neuronal, structural, and behavioral features of animals and humans.
The best robots today are controlled by computers just powerful
enough to simulate the nervous system of an insect, cost as much as
houses, and so find only a few profitable niches in society (among
them, spray painting and spot welding cars and assembling
electronics). But those few applications are encouraging research
that is slowly providing a base for a huge future growth. Robot
evolution in the direction of full intelligence will greatly
accelerate, I believe, in about a decade when the mass-produced
general purpose, universal robot becomes possible. These
machines will do in the physical world what personal computers do in
the world of data—act on our behalf as literal-minded slaves.
The Dumb Robot (ca. 2000-2010)
To be useful in many tasks, the first generation of universal
robots should navigate efficiently over flat ground and reliably and
safely over rough terrain and stairs, be able to manipulate most
objects, and to find them in the nearby world. There are beginnings
of solutions today. In the 1980s Hitachi of Japan developed a
mobility system of five steerable wheels, each on its own telescoping
stalk that allows it to accommodate to rises and dips in uneven
terrain, and to climb stairs, by raising one wheel at a time while
standing stably on the other four. My laboratory at Carnegie Mellon
University in Pittsburgh has developed a navigation method that
enables a robot equipped with sonar range measuring devices and
television cameras to build probabilistic maps of its surroundings to
determine its location and plan routes [Moravec87]. An elegant
three-fingered mechanical hand at the Massachusetts Institute of
Technology can hold and orient bolts and eggs and manipulate a string
in a humanlike fashion [Mason85]. A system called 3DPO from SRI
International in Menlo Park, California can find a desired part in a
jumble seen by special range-finding camera [Bolles84]. The slow
operation of these systems suggests one other element needed for the
universal robot, namely a computer about one one thousand times as
powerful as those found on desks and in robots today. Such machines,
able to do one billion computations per second, would provide robots
approximately the brain power of a reptile, and the personality of a
washing machine.
Universal robots will find their first uses in factories, where they
will be cheaper and more versatile than the older generation of robots
they replace. Eventually they will become cheap enough for some
households, extending the reach of personal computers from a few tasks
in the data world to many in the physical world.
As with computers, many applications of the robots will surprise
their inventors. Some will do light mechanical assembly, clean
bathrooms, assemble and cook gourmet meals from fresh ingredients, do
tuneups on a certain year and make of cars, hook patterned rugs, weed
a lawn, run robot races, do detailed earthmoving and stonework,
investigate bomb threats, deliver to and fetch from warehoused
inventories, and much more. Each application will require its own
original software (very complex by today's computer program
standards), and some may also need optional hardware attachments for
the robot such as special tools and chemical sensors.
Learning (2010-2020)
Useful though they will be, the first generation of universal
robots will be rigid slaves to simple programs. If the machine bangs
its elbow while chopping beef in your kitchen making Stroganoff, you
will have to find another place for the robot to do its work, or beg
the software manufacturer for a fix. Second generation robots with
more powerful computers will be able to host a more flexible kind of
program able to adjust itself by a kind of conditioned learning.
First generation programs will consist primarily of sequences of the
type “Do step A, then B, then C ....” The programs for the
second generation will read “Do step A1 or A2 or A3 ... then B1
or B2 or B3 ... then C1 or C2 or C3 ....” In the Beef Stroganoff
example, A1 might be to chop with the right hand of the robot, while
A2 is to use the left hand. Each alternative in the program has a
“weight,” a number that indicates the desirability of using
it rather than one of the other branches. The machine also contains a
“pain” system, a series of programs that look out for
problems, such as collisions, and respond by reducing the weights of
recently invoked branches, and a “pleasure” system that
increases the relevant weights when good conditions, such as well
charged batteries or a task efficiently completed, are detected. As
the robot bangs its elbow repeatedly in your kitchen, it gradually
learns to use its other hand (as well as adapting to its surroundings
in a thousand other ways). A program with many alternatives at each
step, whose pain and pleasure systems are arranged to produces a
pleasure signal on hearing the word “good” and a pain
message on hearing “bad” could be slowly trained to do new
tasks, like a small mammal. A particular suite of pain- and
pleasure-producing programs interacting with a robot's individual
environment would subtly shape its behavior and give it a distinct
character.
Imagery (2020-2030)
Adaptive robots will find jobs everywhere, and the hardware and
software industry that supports them could become the largest on
earth. But teaching them new tasks, whether by writing programs or
through punishment and reward, will be very tedious. This deficiency
will lead to a portentous innovation, a software world-modeler
(requiring another big increase in computer power), that allows the
robot to simulate its immediate surroundings and its own actions
within them, and thus to think about its tasks before acting. Before
making Beef Stroganoff in your kitchen, the new robot would simulate
the task many times. Each time its simulated elbow bangs the simulated
cabinet, the software would update the learning weights just as if the
collision had physically happened. After many such mental
run-throughs the robot would be well trained, so that when it finally
cooks for real, it does it correctly. The simulation can be used in
many other ways. After a job, the robot can run though its previous
actions, and try variations on them to improve future performance. A
robot might even be configured to invent some of its own programs by
means of a simpler program that can detect how nearly a sequence of
robot actions achieves a desired task . This training program would,
in repeated simulations, provide the “good” and
“bad” indications needed to condition a general learning
program like the one of the previous section.
It will take a large community of patient researchers to build
good simulators. A robot entering a new room must include vast
amounts of not directly perceived prior knowledge in its simulation,
such as the expected shapes and probable contents of kitchen counters
and the effect of (and force needed for) turning faucet knobs. It
needs instinctive motor-perceptual knowledge about the world that took
millions of years of evolution to install in us, that tells us
instinctively when a height is dangerous, how hard to throw a stone,
or if the animal facing us is a threat . Robots that incorporate it
may be as smart as monkeys.
Reasoning (2030-2040)
In the decades while the “bottom-up” evolution of robots
is transferring the perceptual and motor faculties of human beings
into machinery, the conventional Artificial Intelligence industry will
be perfecting the mechanization of reasoning. Since today's programs
already match human beings in some areas, those of 40 years from now,
running on computers a million times as fast as today's, should be
quite superhuman. Today's reasoning programs work from small amounts
of clear and correct information prepared by human beings. Data from
robot sensors such as cameras is much too voluminous and too noisy for
them to use. But a good robot simulator will contain neatly organized
data about the robot and its world. For instance, if a knife is on a
countertop, or if the robot is holding a cup. A robot with simulator
can be married to a reasoning program to produce a machine with most
of the abilities of a human being. The combination will create beings
that in some ways resemble us, but in others are like nothing the
world has seen before.
First Generation Technicalities
Both industrial robot manipulators and the research effort to build
“smart” robots are twenty five years old. Universal robots
will require at least another decade of development, but some of their
elements can be guessed from the experience so far. One consideration
is weight. Mobile robots built to work in human sized spaces today
weigh too many hundreds of pounds. This dangerously large mass has
three major components: batteries, actuators and structure. Lead-acid
batteries able to drive a mobile robot for a day contribute about one
third of the weight. But nickel-cadmium aircraft batteries weigh half
as much, and newer lithium batteries can be half again as light.
Electric motors are efficient and precisely controllable, but standard
motors are heavy and require equally heavy reducing gears.
Ultrastrong permanent magnets can halve the weight and generate high
torque without gears. Robot structure has been primarily aluminum.
Its weight contribution can be cut by a factor of four by substituting
composite materials containing superstrength fibers of graphite,
aramid or the new material Spectra. These innovations could be
combined to make a robot with roughly the size, weight, strength and
endurance of a human.
The first generation robot will probably move on wheels. Legged
robots have advantages on complicated terrain, but they consume too
much power. A simple wheeled robot would be confined to areas of flat
ground, but if each wheel had a controlled suspension with about a
meter of travel, the robot could slowly lift its wheels as needed to
negotiate rough ground and stairs. The manipulation system will
consist of two or more arms ending in dexterous manipulators. There
are several designs in the research labs today, but the most elegant
is probably that of the so-called Stanford-JPL hand (mentioned above,
now found at MIT), which has three fingers each with three controlled
joints. The robot's travels would be greatly aided if it could
continuously pinpoint its location, perhaps by noting the delay from a
handful of small synchronized transmitters distributed in its
environment. This approach is used in some terrestrial and satellite
navigation systems. The robot will also require a sense of its
immediate surroundings, to find doors, detect obstacles and track
objects in its workspace. Research laboratories, including my own,
have experimented with techniques that do this with data from
television cameras, scanning lasers, sonar transducers, infrared
proximity sensors and contact sensors. A more precise sensory system
will be needed to find particular work objects in clutter. The most
successful methods to date start with three dimensional data from
special cameras and laser arrangements that directly measure distance
as well as lateral position. The robot will thus probably contain a
wide angle sensor for general spatial awareness, and a precise, narrow
angle, three dimensional imaging system to find particular objects it
will grasp.
Research experience to date suggests that to navigate, visually
locate objects, and plan and control arm motions, the first universal
robots will require a billion operations per second of computer power.
The 1980s have witnessed a number of well publicized fads that claim
to be solutions to the artificial intelligence or robot control
problem. Expert systems, the Prolog logical inference language,
neural nets, fuzzy logic and massive parallelism have all had their
spot in the limelight. The common element that I note in these
pronouncements is the sudden enthusiasm of group of researchers
experienced in some area of computer science for applying their
methods to the robotics problems of perceiving and acting in the
physical world. Invariably each approach produces some simple
showcase demonstrations, then bogs down on real problems. This
pattern is no surprise to those with a background in the twenty five
year research robotics effort. Making a machine to see, hear or act
reliably in the raw physical world is much, much more difficult than
naive intuition leads us to believe. The programs that work
relatively successfully in these areas, in industrial vision systems,
robot arm controllers and speech understanders, for example,
invariably use a variety of massive numerical computations involving
statistics, vector algebra, analytic geometry and other kinds of
mathematics. These run effectively on conventional computers, and can
be accelerated by array processors (widely available add-ons to
conventional machines which rapidly perform operations on long streams
of numbers) and by use of modest amounts of parallelism. The mind of
the first generation universal robot will almost certainly reside in
quite conventional computers, perhaps ten processors each able to
perform 100 million operations per second, helped out by a modest
amount of specialized computing hardware that preprocesses the data
from the laser eyes and other sensors, and that operates the lowest
level of mobility and manipulation systems.
Mind Children (2050+)
The fourth robot generation and its successors, with human perceptual
and motor abilities and superior reasoning powers, could replace human
beings in every essential task. In principle, our society could
continue to operate increasingly well without us, with machines
running the companies and doing the research as well as performing the
productive work. Since machines can be designed to work well in outer
space, production could move to the greater resources of the solar
system, leaving behind a nature preserve subsidized from space. Meek
humans would inherit the earth, but rapidly evolving machines would
expand into the rest of the universe. This development can be viewed
as a very natural one. Human beings have two forms of heredity, one
the traditional biological kind, passed on strands of DNA, the other
cultural, passed from mind to mind by example, language, books and
recently machines. At present the two are inextricably linked, but
the cultural part is evolving very rapidly, and gradually assuming
functions once the province of our biology. In terms of information
content, our cultural side is already by far the larger part of us.
The fully intelligent robot marks the point where our cultural side
can exist on its own, free of biological limits. Intelligent
machines, which are evolving among us, learning our skills, sharing
our goals, and being shaped by our values, can be viewed as our
children, the children of our minds. With them our biological
heritage is not lost. It will be safely stored in libraries at least;
however its importance will be greatly diminished.
What about life back on the preserve? For some of us the thought of
being grandly upstaged by our artificial progeny will be
disappointing, and life may seem pointless if we are fated to spend it
staring stupidly at our ultra-intelligent progeny as they try to
describe their ever more spectacular discoveries in baby-talk that we
can understand. Is there any way individual humans might join the
adventure?
You've just been wheeled into the operating room. A robot brain
surgeon is in attendance, a computer waits nearby. Your skull, but not
your brain, is anesthetized. You are fully conscious. The robot
surgeon opens your brain case and places a hand on the brain's
surface. This unusual hand bristles with microscopic machinery, and a
cable connects it to the computer at your side. Instruments in the
hand scan the first few millimeters of brain surface. These
measurements, and a comprehensive understanding of human neural
architecture, allow the surgeon to write a program that models the
behavior of the uppermost layer of the scanned brain tissue. This
program is installed in a small portion of the waiting computer and
activated. Electrodes in the hand supply the simulation with the
appropriate inputs from your brain, and can inject signals from the
simulation. You and the surgeon compare the signals it produces with
the original ones. They flash by very fast, but any discrepancies are
highlighted on a display screen. The surgeon fine-tunes the simulation
until the correspondence is nearly perfect. As soon as you are
satisfied, the simulation output is activated. The brain layer is now
impotent—it receives inputs and reacts as before but its output
is ignored. Microscopic manipulators on the hand's surface excise
this superfluous tissue and pass them to an aspirator, where they are
drawn away.
The surgeon's hand sinks a fraction of a millimeter deeper into your
brain, instantly compensating its measurements and signals for the
changed position. The process is repeated for the next layer, and soon
a second simulation resides in the computer, communicating with the
first and with the remaining brain tissue. Layer after layer the brain
is simulated, then excavated. Eventually your skull is empty, and the
surgeon's hand rests deep in your brainstem. Though you have not lost
consciousness, or even your train of thought, your mind has been
removed from the brain and transferred to a machine. In a final,
disorienting step the surgeon lifts its hand. Your suddenly abandoned
body dies. For a moment you experience only quiet and dark. Then,
once again, you can open your eyes. Your perspective has shifted. The
computer simulation has been disconnected from the cable leading to
the surgeon's hand and reconnected to a shiny new body of the style,
color, and material of your choice. Your metamorphosis is complete.
Your new mind has a control labeled "speed." It had been
set at 1, to keep the simulations
synchronized with the old brain, but now you change it to 10,000, allowing you to communicate, react, and
think ten thousand times faster. You now seem to have hours to
respond to situations that previously seemed instantaneous. You have
time, during the fall of a dropped object, to research the advantages
and disadvantages of trying to catch it, perhaps to solve its
differential equations of motion. When your old biological friends
speak with you, their sentences take hours—you have plenty of
time to think about the conversations, but they try your patience.
Boredom is a mental alarm that keeps you from wasting your time in
profitless activity, but if it acts too soon or too aggressively it
limits your attention span, and thus your intelligence. With help
from the machines, you change your mind-program to retard the onset of
boredom. Having done that, you will find yourself comfortably working
on long problems with sidetracks upon sidetracks. In fact, your
thoughts routinely become so involved that you need an increase in
your memory. These are but the first of many changes. Soon your
friends complain that you have become more like the machines than the
biological human you once were. That's life.
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References
[Wiener61] Norbert Wiener, Cybernetics, or Control and
Communication in the Animal and the Machine (second edition), MIT
Press, Cambridge, Massachusetts, 1961.
[Feigenbaum63] Edward Feigenbaum and Julian Feldman, eds.,
Computers and Thought, McGraw-Hill Inc., New York, 1963.
[Bolles84] Robert Bolles, Patrice Horaud and Marsha Jo Hannah, 3DPO: A
Three-Dimensional Part Orientation System, in Robotics Research :
The First International Symposium,, Michael Brady and Richard
Paul, eds., MIT Press, Cambridge, Massachusetts, 1984, pp 413:424.
[Mason85] Matt Mason and Kenneth Salisbury, Robot Hands and the
Mechanics of Manipulation, MIT Press, Cambridge, Massachusetts,
1985.
[Moravec87] Hans Moravec, Sensor Fusion in Certainty Grids for Mobile
Robots, AI Magazine v9#2, Summer 1988, pp 61-77.
[Moravec88] Hans Moravec, Mind Children: The Future of Robot and
Human Intelligence, Harvard University Press, Cambridge,
Massachusetts, 1988.
[Lenat89] Douglas Lenat and Rajiv Guha, Building Large
Knowledge-Based Systems: Representation and Inference in the Cyc
Project, Addison-Wesley Publishing Co., Reading, Massachusetts,
1989.
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Figure Caption
A caricature of
a first-generation universal robot, showing wheels on telescoping
legs, arms with dexterous hands, camera eyes for object-finding and an
implied spatial awareness and navigation system, all operated by a
billion operation per second onboard computer.
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Biography
Hans Moravec has been thinking about machines thinking since he was a
child in the 1950s. He built his first robot, a construct of tin
cans, batteries, lights and a motor, at age ten. In high school he
won two science fair prizes for a light-following electronic turtle
and a tape-controlled robot hand. As an undergraduate he designed and
began construction of a computer to control more sophisticated robots,
and then experimented with learning and automatic programming
techniques when commercial computers became available to him. During
his master's work he built a small robot with photoelectric eyes and
other sensors controlled by a minicomputer, and wrote a thesis
outlining a computer language for artificial intelligence. He
received a PhD from Stanford in 1980 for a TV-equipped robot, remote
controlled by a large computer, that successfully negotiated cluttered
obstacle courses to arrive at desired destinations. Since 1980 he has
been director of the Carnegie Mellon University Mobile Robot
Laboratory, birthplace of three mobile robots that navigate cluttered
spaces with a sense of spatial awareness derived from data from
cameras, sonars, and other sensors.
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