Hi everyone,
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In 1994,scientists used a robot named DANTE-II to monitor volcanic activity in Alaska's Mount Spurr.the spider-like robot was controlled by commands sent by satellite from Anchorage,many miles away.DANTE-II's mission was to creep up to the edge of the volcano which had errupted in 1992 &1993 and peer over volcano's edge to transmit images to scientists.DANTE-II used a laser imaging system and a camcorder to transmit images to satellites.these images were then forwarded by satellite to Anchorage and Ames Research Center(CALIFORNIA).

Robots have also been used to study underwater enviornments.oceanographers used a robot,compuers communication techniques to explore GULF of CALIFORNIA.the robot was controlled by a nearby ship.

Robots have also been used in Space Exploration Missions.eg PATHFINDER.
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RECENTLY:
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these are several experimental robots already being used successfully:
1.)"AIBO" dog created by sony corporation.
2.)"TEKNO" a dog similiar to aibo,has intelligence of pup of about 8 days.i bought it for my cousin sister(she's 3)and it was pretty good.
3.)"DOTTIE".it is a robot created by Cyberclean.a company that is going after $50 bilion industrial cleaning market.it is used for cleaning purposes.but can they do it safely? using video sensors,DOTTIE observes simple rules to avoid any damage,such as stopping and waiting if somebody walks in front of her.when her work is finished she goes back to her charging station.

in 1997 annual markets for robots passed $1 billion dollar mark.increasingly,robots are performing tasks such as assembly,welding,material handling,and material transport.according to a recent estimate more than 1 million industrial robots will be in use by almost 2005.
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so where's the personal robot,which will someday mow the lawn,clean the house,and shampoo the carpets??
robots are too expensive right now,but they"ll not be.just wait and watch.;)
BYE!

Robots:are We Close Yet?

NYET....

we are coming close(or at least these developments say so)

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VERTICALLY CHALLENGED
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imagine a robot small enough to crawl through pipes to check for chemical leaks or sneak under doors to spy on intruders.researchers at SANDIA NATIONAL LABS
have created the mini Autonomous robot vehicle Jr. to do just that.smaller than a cherry and powered by three watch batteries.MARV Jr. can cover 50cm per min.on custom made tracks fashioned from strips of latex balloons.future versions may include miniature cameras,mics,chemical microsensors.etc
for more details see:
www.sandia.gov/isrc/Marv.html
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MODEL EMPLOYEE :
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she's always on time,she ne'r complains.she's nice looking robot called CoWorker,the office robot.about 1m high,this pentium powered robot uses sonar sensors to keep her from bumping to walls and people as she rolls along languid 1,6 km an hour. a digital camera perched atop her keeps rotating.craneline neck can wirelessly transmit pictures of remote assembly lines,construction sites etc.

check out:
www.irobot.com for more details.

STILL, I SAY, NYET......:D

hahahahahahahahahahahahahah........
i just fell off my chair KM.

NYET.YES.:p :p :p :p :p :p

There's still a long way to go if robots can't even figure out that it's not a good idea to climb a volcano.

Originally posted by rde
There's still a long way to go if robots can't even figure out that it's not a good idea to climb a volcano.
hi,
and that is why i intend to use them so much untill they become intelligent.

I would like to develop a Dolphin translator (either Human to Dolphin to Human, or Text to Dolphin to Text). Any ideas?

I kind of understood a Dolphin once in a Nature program about those in the Florida Keys. From what I saw of the footage and the sounds I got the impression that dolphins sounds aren't the only thing they use. They shake their heads and snout in the direction of an object and squeak to say something is up ahead, they can even move their head and speak to tell other dolphins to move left or right of an object.

They also act with curiousness because they don't want to be percieved a threat because that is an excellent survival method.

I did have a thought that they might actually have some sort of telepathic connection, using their squeaks resonating along their jaws to communicate with other dolphins.

Of course this means that their Sonar is a very important evolutionary step.

I thought I would just put those points to you Kmguru, just incase you ever find out if their is any truth in it.

Though it was not published, people thought that Whales too communicate via telepathy over 4000 miles. It turns out that they can comminicate via low frequency sound over that distance using a specific layer of ocean that does not degrade the sound waves.

So, it is more likely that Dolphins use similar means. But you never know. My resident psychic says that by 2030, I should have developed a revolutionary transporation device that is like StarTrek teleporter but not exactly the same method.

Thanks for the input...

Talking via telepathy,i think is the only solution to interspecies communication problem.that would really eliminate the need for us to understand the language of various other species.a brain attatched with transmitter will transmit at certain frequencies messages,the receiver on the other hand will decode the message and understand it.i presume that brain already has a receiver in form of cerebral cortex,only problem is transmission of message.i dont know much about brain though but there must be a mechanism for doing so,if telepathy is a reality,anyway i would like to be more enlightened on this subject untill i speak more.
bye!

Monkeys in North Carolina have remotely operated a small robotic arm 600 miles away in MIT's touch lab using their brain signals.they did this with the help of 96 electrodes(each one less than diameter of a single human hair)embedded into their brains.the electrodes detected the brain signals of these animalsand transmitted them across the globe to actuate the robotic arm to fetch the food.


Scientists from Duke university medical Center,MIT and State University of NY health science center believes that this brain-machine interface can used by paralysed patients to contrl their limbs.

it makes me wonder although about their wider application in communication process.

I am not so sure I would want my thoughts televised without my consent.
Isn't the cell phone enough??????

My understanding of Telepathy is that it is instaneous communication between subjects. More like quantum communication. Hooking up an ordinary electronic transreceiver (telephone) to brain still will require proper infrastucture (wireless or wire) and it will take time to send signal to Mars.

*Originally posted by kmguru
My resident psychic says that by 2030, I should have developed a revolutionary transporation device that is like StarTrek teleporter but not exactly the same method. *

Aside from the fact that your resident psychic is nuts, make sure you let us know when you plan to use it for the first time.

I'll pay as much as $10, or the 2030 equivalent, to watch you fry yourself in a glorified microwave.

hi Tony1,

we are in this forum,because we like to speculate,fantasize the possible aspects of teckno,no you"ll not disect this post.please,give us your view point about the technology,i hope you understand as i dont want thread to turn up as relegion,useless disections of posts,arguments...


JESUS IS LORD TONY1,I AM NOT,YOU ARE NOT,WE COMMIT MISTAKES AND SO DO YOU,SO DONT THINK PEOPLE LIKE YOU FOR ALL THOSE DISSECTIONS AND ALL THOSE MESSAGES THAT YOU THINK ARE INTELLIGENT...TRY TO GIVE US POSITIVE INPUTS,DONT FORCE IT ON US,SICE JESUS NEVER FORCED ANYTHING HE GAVE US IDEAS TO THINK ABOUT...

BYE!

I don't know what Tony1 is worried about, this sort of equipment is how his religion started and how it's not going to end, but begin.

Everything you've ever read in your Bible Tony1 isn't there because a god put it there, it's because it got Data cracked and punched through relativity to formulate a repeat.

Never stumble into our domain, looking to weaken our morale against what you deem as absurd creations, just because your Lord didn't create them doesn't make them as Lame.

Don't insue that Psychics know nothing, afterall they might know where you live and grant you that Hologram you've been waiting for. (You know the one... bleeding without wounds)

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. An entirely different approach, named Artificial Intelligence (AI), attempted to harness the apparently prodigious power of post-war computers--able to do the arithmetical work of thousands of mathematicians--to more interesting kind of thinking. And indeed, by 1965, computers ran programs that proved theorems in logic and geometry, solved calculus problems and played good games of checkers. In the early 1970s, AI research groups at MIT 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 physical 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 an average 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 supercomputers worth.


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.


First-Generation Universal Robots
Timeframe: 2000-2010
Processing power: 1,000 MIPS (1993 supercomputer -- Reptile-class)
Distinguishing feature: General-purpose perception, manipulation and mobility


A robot's activities are assembled from its fundamental perception and action repertoire. First-generation robots will exist in a world built for humans, and that repertoire most usefully would resemble a human's. The general size, shape and strength of the machine should be human-like, to allow passage through and reach into the same spaces. Its mobility should be efficient on flat ground, where most tasks will happen, but also reliable and safe over stairs and rough ground, lest the robot be trapped on single-floor "islands." It should be able to manipulate most everyday objects, and to find them in the nearby world. The components of this machine exist in laboratories worldwide, and suggest guidelines for a practical design this decade.


1,000 MIPS (Millions of Instructions Per Second) is just enough computing power for a moving robot to maintain a coarse map of its surroundings and use it for locating itself relative to trained itineraries and to plan and control driving. When not traveling, there is power enough to construct a fine map of a manipulator workspace, to locate particular objects and to plan and control arm motions. When not occupied with its unique robotic functions, the robot should share with personal computers of its time the ability to communicate over wireless networks, to generate and interpret spoken sentences and to generate and read printed text. Programs for specific applications--many obtained via high-speed networks--will orchestrate these basics to accomplish useful tasks.


Universal robots will find their first uses in factories, warehouses and offices, where they will be more versatile than the older generation of robots they replace. Because of their breadth of applicability, their numbers should grow rapidly, and their costs decline. Eventually they will become cheap enough for some households, extending the utility of personal computers from a few tasks in the data world to many in the physical world. Perhaps a program for housecleaning will be included with each robot, as word-processing programs were shipped with early personal computers.


As with computers, some applications of robots will surprise their manufacturers. Robot programs may be developed to do light mechanical work (assembling other robots, for example), deliver warehoused inventories, prepare specific gourmet meals, tune up certain types of car, hook patterned rugs, weed lawns, run races, play games, arrange earth, stone and brick or sculpt. Some tasks will need specialized hardware attachments like tools and chemical sensors. Each application will require its own original software, very complex by today's computer program standards. The programs will contain modules for recognizing, grasping, manipulating, transporting and assembling particular items--modules developed via learning programs on supercomputers (with about 100,000 MIPS). In time, a growing library of subtask modules may ease the construction of new programs.


A first-generation robot will have the brain power of a reptile, but most application programs will be so hard pressed to accomplish their primary functions that they will endow the robot with the personality of a washing machine.


Second-Generation Universal Robots
Timeframe: 2010-2020
Processing power: 30,000 MIPS (Mammal-class)
Distinguishing feature: Accommodation learning


First-generation robots will be rigid slaves to inflexible programs, relentless in pursuing their tasks--or repeating their errors. Their programs will contain the frozen results of learning done on bigger computers under human supervision. Except for specialized episodes like recording a new cleaning route or the location of work objects, they will be incapable of learning new skills or adapting to unanticipated circumstances--even modest alterations of behavior will require new programming, probably from the original software suppliers.


Second-generation robots, with thirty times the processing power, will be more adaptable, because they can do some learning onboard. The fundamental idea in adaptive learning is to "close the loop" on behavior: to evaluate each action's effect in a given context to enhance the process that generated the action. In the simplest technique, a behavioral alternative that succeeds becomes more likely to be invoked in similar circumstances, while an alternative that fails becomes less probable. Faster statistical-learning approaches like neural nets repeatedly tweak behavior-control parameters to nudge actual responses closer to an ideal. Programs for second-generation robots will use many such learning techniques, creating new abilities--and new pitfalls.


If a first-generation robot working in your kitchen runs into trouble--say, failing to complete a key step because a portion of the workspace is awkwardly small--you have to option of abandoning the task, changing its environment, or somehow obtaining altered software that accomplishes the problematic step in a different way. A second-generation robot will make a number of false starts, but most probably will find its own solution, adjust to its home in thousands of more subtle ways, and gradually improve its performance. While a first-generation robot's personality is determined entirely by the sequence of operations in the application program it runs at the moment, a second-generation robot's character is more a product of the suite of conditioning programs it hosts. The conditioning system might, in time, censor an entire application program, if it gave consistently negative results.


Second-generation robots of 2010 will have onboard computers as powerful as the supercomputers that learned for first-generation machines in 2000. But by 2010, supercomputers will be proportionally more powerful (about 3,000,000 MIPS), and will themselves play a background role for the second-generation. The many individual programs of a conditioning suite--each responding to some specific stimulus--interact with one another and with the robot's control programs and environment in ways that will be far too entangled to anticipate accurately. It would be possible to evaluate particular suites by trying them out in robots--the acid test in any case--but that would be a slow and dangerous way to sift a large number of rough candidates--some would certainly behave in unexpected ways that could damage the robot, or even endanger the testers.


Faster and safer initial screenings might be done in factory supercomputer simulations of robots in action. To be of value, simulations would have to be good models, predicting accurately such things as the probability that a given grip can lift a particular object, or that a vision module can find a given something in particular clutter. Simulating the everyday world in full physical detail will still be beyond computer capacity in 2010, but it should be possible to approximate the results by generalizing data collected from actual robots: essentially to learn from the working experience of real robots how everyday things behave. A large systematic collection effort under human supervision will probably be necessary lest there be too many gaps or distortions. A proper simulator would contain at least thousands of learned models for various basic interactions (call them interaction primitives), in what amounts to a robotic version of common-sense physics.


Third-Generation Universal Robots
Timeframe: 2020-2030
Processing power: 1,000,000 MIPS (Primate-class)
Distinguishing feature: World modeling


Adaptive second-generation robots will find jobs everywhere, and may become the largest industry on earth. But teaching them new skills, whether by writing programs or through training, will be very tedious. A third generation of universal robot, with onboard computers as powerful as the supercomputers that optimized second-generation programs, will learn much faster because they do much of the trial and error in fast simulation rather than slow and dangerous physicality. Once again, a process done by human-supervised supercomputers at the factory in one robot generation will be improved and installed directly onboard the next generation, and once again new opportunities and new problems will arise.


With a simulator onboard, it becomes possible for a robot to maintain a running account of the actual events going on around it--to simulate its world in real time. Doing so requires that almost everything the robot senses be recognized for the kind of object it is, so that the proper models of interaction can called up. Recognizing arbitrary objects by sight is as difficult as knowing how they will interact: it will require modules specially trained for each kind of thing (call them perception primitives). Some perception primitives may already have been developed for second-generation factory simulators, to help automate the tedious job of creating simulations of robot workspaces, but an additional effort to fill gaps and systematize them will surely be necessary to prepare them for fully automatic use in the third generation. Perception primitives will allow a robot's three-dimensional map of a room to be transformed into a working model, as each object is identified and linked with its proper interaction primitives.


A continuously updated simulation of self and surroundings gives a robot interesting abilities. By running the simulation slightly faster than real time, the robot can preview what it is about to do, in time to alter its intent if the simulation predicts it will turn out badly--a kind of consciousness. On a larger scale, before undertaking a new task, the robot can simulate it many times, with conditioning system engaged, learning from the simulated experiences as it would from physical ones. Consequently well trained for the task, it would likely succeed the first time it attempted it physically--unlike a second-generation machine, which must make all its mistakes out in real life. When it has some spare time, the robot can replay previous experiences, and try variations on them, perhaps learning ways to improve future performance. A sufficiently advanced third-generation robot, whose simulation extends to other agents--robots and people--would be able to observe a task being done by someone else, and formulate a program for doing the task itself: it could imitate.


Though they will be able to adapt, imitate and create simple programs of their own, third-generation robots will still rely on externally supplied programs to do complicated jobs. Since their motor and perceptual functions will be quite sophisticated, and their memories and potential skills large, it will be possible to write wonderfully elaborate control programs for them, accomplishing large jobs, with nuances within nuances. It will be increasingly difficult for human programmers to keep track of the many details and interactions. Fortunately, the task can be largely automated. Shakey, the first computer-controlled mobile robot, developed at SRI in the late 1960s, had at its heart a reasoning program called STRIPS (STanford Research Institute Problem Solver) that expressed the robot's situation and capabilities as sentences of symbolic logic, and solved for the sequence of actions that achieved a requested result as a proof of a mathematical theorem. In 1969, on computers with a mere 0.1 MIPS, neither the theorem prover nor the sensory processing which provided its input could handle the complexity of realistic situations, and Shakey was limited to maneuvering around a few blocks. Nevertheless, the idea was sound: given a correct description of the initial and desired state of the world, and enough time and space to work, a theorem prover will find an absolutely correct solution, of whatever generality, subtlety and deviousness is required, if one exists at all. By the time of the third universal robot generation, supercomputers will provide 100,000,000 MIPS, and (thanks to continuing progress in the top-down Artificial Intelligence industry) programs will exist which will be able to STRIPS-like reasoning with real world richness. So factory supercomputers in 2025 will accept complex goals (find a sequence of robot actions which assembles the robot described in the following design database), and compile them via theorem provers into wonderfully intricate control programs for third-generation robots, which will, in turn adapt them to their actual circumstances.


Fourth-Generation Universal Robots
Timeframe: 2030-2040
Processing power: 3,000,000 MIPS (Human-class)
Distinguishing feature: Reasoning


In the decades while the "bottom-up" evolution of robots is slowly 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 unambiguous 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 and labeled descriptions of the robot and its world, ready to answer questions from a reasoning program asking, for instance, if a knife is on a countertop, or if the robot is holding a cup, or even if a human is angry


Fourth-generation universal robots will have computers powerful enough to simultaneously simulate the world, and reason about the simulation. Like the factory supercomputers of the third-generation, fourth generation robots will be able to devise ultra-sophisticated robot programs, for other robots or for themselves. Because of another gift from the Artificial Intelligence industry, they will also be able to understand natural languages. While the original language understanders will probably use a verbal common-sense database similar to the one being developed by the Cyc project, where the meaning of words is defined in reference only to other words, in a fourth-generation robot some concepts and statements will be understood more deeply, through the action of the simulator. When someone tells the robot "the water is running in the bathtub" the robot can update its simulation of the world to include flow into the unseen tub, where a simulated extrapolation would indicate an undesirable overflow later, and so motivate the robot to go to turn off the tap. A purely verbal representation might accomplish the same thing if it included the statements such as "A filling bathtub will overflow if its water is not shut off," but a modest number of general principles in a simulator, interacting in combinations, can provide the equivalent information of an indefinite number of sentences. Similarly a reasoning program, making inferences about physical things, might be enhanced by a simulator: candidate inferences would rejected if they failed in a parallel simulation of a typical case, and, conversely, persistent coincidences in the simulation could suggest statements that can be proved--the robot would be visualizing as it listened, spoke and reasoned. A modest but very successful version of such an approach was used in one of the earliest Artificial Intelligence programs, a geometry theorem prover by Herbert Gelernter in 1959. Starting with the postulates and rules of inference in Euclid's "Elements," Gelernter's program proved some of the theorems, using algebraic "diagrams" to eliminate false directions in the proofs. Before attempting to prove two triangles congruent in a certain construction, for instance, the program would generate an example of the construction, using random numbers for the unspecified quantities, and measure the resulting triangles. If they were not sufficiently similar--within the precision of the arithmetic--the program abandoned that approach and tried something else.


Simulator-augmented language understanding and reasoning may be so effective in robots that it will be adopted for use in plain computer programs, "grounding" them in the physical world via the experiences of the robots that tuned the simulators. In time the distinction between robot controllers and disembodied reasoners will diminish, and reasoning programs will sometimes link to robot bodies to interact physically with the world, and robot minds will sometimes retire into large computers, to do some intense thinking off-line.


A fourth-generation robot will be able to accept statements of purpose from humans, and "compile" them into detailed programs that accomplish the task. With a database about the world at large the statements could become quite general--things like "earn a living", "make more robots" or "make a smarter robot." In fact, fourth generation robots will have the general competence of human beings, and resemble us in some ways, but in others be like nothing the world has seen before. As they design their own successors, the world will become ever stranger.


The Short Run (early 2000s)
As the industrial revolution gathered steam two centuries ago, it destroyed cottage industries and concentrated wealth in he hands of factory owners--the capitalists. Millions of displaced home workers competed for too few jobs tending the new machines. It took difficult political readjustments to equalize the benefits of cheaper, more plentiful goods, but gradually laborers' hours were halved, creating need for more workers, and so bidding up salaries. Though it increases communal wealth, each increment in automation threatens a similar unpleasant transient, as it displaces one group of workers with fewer doing different tasks. If the new required skills are common, mass competition for the few jobs drives down salaries. If the skills are rare, scarcity encourages high pay and long hours. Either way, some work excessively while others are jobless--and it takes slow changes in the social contract and in education to level the load.


Though work hours will decline, they cannot be the final answer to rising productivity. In the next century inexpensive but capable robots will displace human labor so broadly that the average workday would have to plummet to practically zero to keep everyone usefully employed. Already, much labor services more questionable needs--gargantuan government bureaucracies, cosmetic medicine, mass entertainment, and speculative writing, to give a few examples. In time almost all humans may work to amuse other humans, while robots run competitive primary industries, like food production and manufacturing. There is a problem with this picture. The "service economy" functions today because many humans willing to buy services work in the primary industries, and so return money to the service providers, who in turn use it to buy life's essentials. As the pool of humans in the primary industries evaporates, the return channel chokes off--efficient, no-nonsense robots will not engage in frivolous consumption. Money will accumulate in the industries, enriching any people still remaining there, and become scarce among the service providers. Prices for primary products will plummet, reflecting both the reduced costs of production, and the reduced means of the consumers. In the ridiculous extreme, no money would flow back, and the robots would fill warehouses with essential goods which the human consumers could not buy.


The scenario above is incomplete. Not all individuals involved in productive enterprises actually work there. Stockholders, having once contributed capital to a thriving enterprise, may collect dividends indefinitely. Workers can be replaced by something more efficient, but in the present legal system, owners remain unless they sell out. Even with total automation, human business proprietors will continue to profit, and so be able to patronize the service providers. An analogous situation existed in classical and feudal times, where an impoverished, overworked majority of slaves or serfs played the role of robots, and land ownership played the role of capital. In between the serfs and the lords, a working population struggled to make a living from secondary sources, often by performing services for the privileged. The most prestigious and prosperous commoners sold high quality products and services directly to the gentry (as in the proud line still seen in Britain, By Appointment to Her Majesty). A larger number lived less well by trading with other townspeople.


It is unlikely that a future majority of service-providing "commoners" with more free time, communications and democracy than today, would tolerate being lorded over by a minority of non-working hereditary capitalists: they would vote to change the system. The trend in the social democracies has been to equalize income by raising the standards of the poorest as high as the economy can bear--in the age of robots, that minimum will be very high. In the early 1980s James Albus, head of the automation division of the then National Bureau of Standards, suggested that the negative effects of total automation could be avoided by giving all citizens stock in trusts that owned automated industries, making everyone a capitalist. Those who chose to squander their birthright could work for others, but most would simply live off their stock income. Even today, the public indirectly owns a majority of the capital in the country, through compounding private pension funds. In the United States, universal coverage could be achieved through the social security system. Social security was originally presented as a pension fund that accumulated wages for retirement, but in practice it transfers income from workers to retirees. The system will probably be subsidized from general taxes in coming decades, when too few workers are available to support the post World War II "baby boom." Incremental expansion of such a subsidy would let money from robot industries, collected as corporate taxes, be returned to the general population as pension payments. By gradually lowering the retirement age towards birth, most of the population would eventually be supported. The money could be distributed under other names, but calling it a pension is meaningful symbolism: we are describing the long, comfortable retirement of the entire original-model human race.


The Medium Run (around 2050)
What happens to people when work becomes passe? Existing retirement communities are probably too sleepy to be a good model--most of the individuals there have completed their life's work, and are of declining vigor and health. Better examples may be the richest Arabian petro-kingdoms, where oil-bought foreign labor plays the role of total automation. In a tradition of tribal sharing shaped by a sparsely-furnished nomadic past, Kuwait, Saudi Arabia and the United Arab Emirates have managed to spread the new wealth broadly among the citizenry in a single generation. Free health care and education, and undemanding government jobs, or outright welfare, secure life's needs, and life expectancies and literacy rates are among the world's highest. Comfort and security mute the stresses of civilization, including the tension between circumscribed Islamic values and the liberties of a wealthy world culture. The societies produce both world-class achievers and criminals, but on average show less driven urgency than many industrialized nations: most of their citizens seem happy to simply live their lives, and stability is endangered only by neighboring countries, where impoverished majorities are less content with the status quo. In numerous smaller examples, wealthy families often produce generations of content, even smug, heirs (as well as exceptions to titillate the tabloids).


Contrary to fears of some enmeshed in civilization's work ethic, our tribal past prepared us well for lives as idle rich. In a good climate and location the hunter-gatherer's lot can be pleasant indeed: an afternoon's outing picking berries or catching fish--what we civilized types would recognize as a recreational weekend--provides life's needs for several days. The rest of the time can be spent with children, socializing or simply resting. Of course, our ancestors had also to survive hard times, and evolution bequeathed us the capacity for desperate measures, including hard work. Civilization turned that extremity into everyday normality, and now stress is the leading cause of disease, and probably triggers some of the ugliest aspects of tribalism. In primates, overpopulation is a common reason for group distress, as nature-provided food and shelter falls short. To survive, a strong tribe may chase away or exterminate a weaker neighbor, or drive out or otherwise eliminate some of its own members: maybe those who smell, look, sound or act differently. Sometimes stressed individuals become accident or disease prone, and die spontaneously, improving the prospects for their relatives--similar considerations could regulate the prevalence of non-reproductive behaviors like homosexuality. City life, absurdly crowded and stressful by tribal-village standards, may inappropriately activate unconscious overpopulation reflexes--the self-destructive emotional vehemence of ethnic strife hardly reflects rational self-interest. It will harder to stir up battle fervor against minorities from the luxurious lassitude of a robot-supported life.


Ultra-conservative Switzerland may be a hint of things to come. Government and commercial institutions perfected through centuries of peace (interrupted only briefly by Napoleon) have given Switzerland unmatched prosperity, stability and security. Most Swiss citizens work, but they do so comfortably, with generous government welfare--and Italian immigrant labor--having lessened the desperation that forces workers elsewhere into unpleasant jobs. Comfortable prosperity has allowed multi-ethnic, multi-religious, multi-language Switzerland, made of 23 fiercely independent Cantons each with its own traditions and history, to peacefully endure the most severe internal differences of opinion, for instance the political fury between German and French factions during the first World War. The average Swiss citizen may resist most major changes (why ruin a good thing?), but Switzerland produces world-class contributors in all fields--if a bit less flamboyance than average. While it gives everyone the opportunity to excel, it lacks the social trauma that drives some other countries. Few Swiss would prefer it otherwise.


Many trends in industrialized countries presage a future of humans supported by a rich robot economy, as our ancestors were supplied by their ecology (call it paradise with plumbing). Technology and global competition are gradually depopulating businesses. Even absent universal robots, increasingly flexible automation is displacing labor in food production and manufacture, while communicating computers are replacing clerks, secretaries, and managers in offices. Jobs that still require human labor are moving to the homes of computer-equipped "tele-commuters" (like this author) who report reduced stress and improved family life. In a ripple effect, smaller work staffs imply less catering, janitorial and maintenance support. In future, as smarter computers, able to handle policy making, public relations, law, engineering and research replace the last telecommuters, and as capable robots displace technicians, janitors, vehicle drivers and construction crews, it will only be common sense for a population to vote itself income from taxes on labor-free but superbly productive industries. Less developed countries might rapidly catch up by offering the same industries location and raw materials at lower tax cost--a trained population will no longer be a requirement.


Western democracies may come to resemble lazier Switzerlands, but with large differences. Big cities will lose their economic advantages, and may begin to evaporate, as individuals, linked to the world by high fidelity communications and served by personal robots, scatter to areas offering more elbow room. Large countries may similarly become less important, as taxes on local industries, and local robot labor, become adequate to supply all human needs. The civilized world may again return to a comfortable tribalism, after a five millennium detour into organized civilization. Countries with traditional tribal structures may simply stay that way, building on their ancestral customs, leapfrogging urbanization altogether, while developed countries foster tribes with customs and beliefs that exceed even today's notion of bizarre.


Tribalism will express itself in entirely novel ways. Over the last two decades, inter linked computer networks have hosted small communities whose members happen to be distributed around the world. In 1993 the informal "Usenet" had about ten million subscribers, carrying on about three thousand specialized discussions on every conceivable topic, some with fifty page-long messages every day. Regular contributors to a particular "newsgroup" soon begin to recognize one another, and develop characteristic interactions, likes and dislikes. They form factions that praise, recruit, condemn and ostracize. When a newsgroup grows too large and noisy, specialized subgroups are formed, reducing the original group's population. In future, the world networks will have much greater capacity, and new abilities, such as language translation. "Tribes of common interest" will share more than written text, perhaps exchanging voice and video, or manifesting themselves in full sensory 3D, in synthesized virtual realities: tribal lands that exist in the minds of computers, in greater number, variety and accessibility than possible in the physical world.


While computer simulations create entirely new worlds, robots will transform physical life. Today, manufactured items are difficult to make, and thus relatively rare and expensive, and we expend great effort in acquiring and defending them--our homes are fortified warehouses of our possessions. Stockpiling will be less appropriate amidst robotic abundance: why hoard fruit in an orchard? Conventional manufacturing methods--molding, casting, milling, assembly--can be robotically orchestrated to make new items fairly quickly. Even better, robotic accuracy and patience can build up solid objects by precisely "painting" various materials, layer upon cross-sectional layer. Such new approaches, refined to molecular resolution (done now modestly in scanning tunneling microscopes) will produce arbitrary solid objects from computer descriptions. Humans may be able to live in uncluttered spaces--in ecological preserves, if they choose--yet have any needed item, perhaps even food or housing, made on the spot, or delivered from small local caches--then disassembled back into into raw materials after use. The most visible technological products remaining may be robots themselves, in various sizes and shapes, and these may lurk unobtrusively until called upon.


Robots that live among humans, providing goods and services, will themselves be consumer products, styled, outfitted and programmed to please the customers. They will be manufactured by very different robots that extract energy and raw materials and perform major engineering, exploration and research projects. Molded by the constraints of the physical world rather than by human whim, these worker machines are likely to become ever more varied in size, shape and function, forming an entire ecology of artificial life that will eventually surpass the existing biosphere in diversity. The first fully automated companies, evolved from existing firms, will be in familiar industrial settings near population centers. As human labor becomes superfluous, economics will dictate cheaper sites, perhaps locations that humans find unpleasant because they are too hot, too cold, too dry, too poisonous, too far underground or too remote.


Robot companies will be shaped by future editions of existing laws, by taxes, and by consumer demand. Existing laws give incorporated entities some of the rights of a person, most importantly the right to own property and make contracts. They do not grant the right to life--corporations may legally be killed by competition or through legal or financial actions. Corporations are bound by laws similar to those that regulate humans, and can be punished through fines, operating restrictions or dissolution--even without humans to fine, imprison or execute. Corporations stay alive by building and maintaining physical assets that generate income to pay their expenses. In the mid 21st century, the biggest expense will be taxation, and income will come mostly from choosy human customers.


Tax laws will be shaped by human voters: there is no precedent or motivation for extending suffrage to robots, and the vote will be one of the very few advantages humans retain. Some debate is inevitable, but there should be few qualms about keeping even very superior thinking machines in disenfranchised bondage. It takes force, indoctrination and constant vigilance to counter inherited needs and motivations and enslave a human, but a robot can be constructed to enjoy the role. Natural evolution itself has provided examples, in worker castes of social insects, and self-sacrificing mothers of all species.


The primary job of voters in the next century will be protecting their retirement benefits, that is ensuring that robot industries continue to support them. The robots will present a moving target, but the instruments of control will also grow in power. Not only will companies that get out of line be liable for punishment--if necessary, by force purchased from other companies--but they can be controlled a-priori by intrusions directly into their software.


Corporate intelligences may be governed by structures like those controlling fourth-generation robots. Immensely powerful reasoning and simulation modules will plan complex actions, but the desirability of possible outcomes will be defined by much simpler positive and negative conditioning modules (or by sets of axioms in super-rational systems), whose composition shapes the character of the entire entity. Humans can buy enormous safety by mandating an elaborate analog of Isaac Asimov's three "Laws of Robotics" in this corporate character--perhaps the entire body of corporate law, with human rights and anti-trust provisions, and appropriate relative weightings to resolve conflicts. Robot corporations so constituted will have no desire to cheat, though they may sometimes find creative interpretations of the laws--which will consequently require a period of tuning to insure their intended spirit.


Internalized laws, properly adjusted, should produce extraordinarily trustworthy entities, happy to die to ensure their legality. Even so, accident, unintended interactions or human malice could occasionally produce a rogue robot or corporation, with superhuman intelligence and unpleasant goals. "Police" clauses in the core corporate laws, inducing legal corporations to collectively suppress outlaws, by withholding services, or even with force, would mitigate the danger. Overall safety would be enhanced by anti-trust provisions that limit collusion and cause overgrown corporations to divide into competing entities, ensuring diversity and multiplicity. In the next section we discuss activities in the solar system that could threaten Earth: in response, police clauses might be expanded in scope to support a planetary defense.


Like basic food in today's developed countries, common manufactured goods in the next century will be too cheap and plentiful to be very profitable. To pay their taxes, most companies will be forced to continually invent unique products and services in a race against competitors to attract increasingly sophisticated (or jaded) human consumers. Automated research, as superhumanly systematic, industrious and speedy as robot manufacturing, will generate a succession of new products, as well as improved robot researchers and models of the physical and social world. The likely results will exceed the dreams of science fiction: robotic playmates, virtual realities and personalized works of art that stir the emotions like nothing before, medical solutions for every physical, mental or cosmetic whim, answers to satisfy any curiosity, luxury visits anywhere in the solar system, and things yet to be imagined. The existence of an astronomically increasing variety of consumer choices will accelerate the divergence of human tribes: some may choose a comfortable imitation of an earlier period (as the Amish today), but others will push the human envelope in wisdom, pleasure, beauty, ugliness, spirituality, banality and every other direction. The choices made by diverse communities will shape robot evolution--only companies able to devise services of interest to the customers will generate enough income to survive.


Humans too will be shaped by the relationship. Robot services will be inexpensive, but not free, and income will be finite. Corporations will operate globally, but taxes will increasingly be assessed on and redistributed on a tribal scale. Tribes that tax too heavily will drive away the corporations, and so eliminate their revenue--like tribes of the past that overburdened their ecology, they will learn modesty of expectation. More subtly, corporations struggling to appeal to consumers will develop and act on increasingly detailed and accurate models of human psychology. The superintelligences, just doing their job, will peer into the workings of human minds--and manipulate them with subtle cues and nudges, like adults redirecting toddlers.


Prosperity beyond imagination should eliminate most instinctive triggers of aggression, but will not prevent an occasional individual or group from deciding to make mischief for others. Serious trouble can be avoided by restricting robotic technology, since mere human actions will not be very dangerous in a world where cheap superhuman robots function as sleepless sentries, prescient detectives, fearless bodyguards, and, in extremis, physicians able to reconstitute live humans from fragments or digital recordings. To be effective, inbuilt laws that prevent corporations from directly contributing to mayhem must also include clauses limiting the powers they can sell to people.


Both biological and hard robotic technologies can be used to enhance human beings. Such present-day examples as hormonal and genetic tuning of body growth and function, pacemakers, artificial hearts, powered artificial limbs, hearing aids and night vision devices are faint hints of future possibilities. In Mind Children, I speculated on ways to preserve a person while replacing every part of body and brain with a superior artificial substitute. A biological human, not bound by corporate law, could grow into something seriously dangerous if transformed into an extensible robot. There are many subtle routes to such a transformation, and some will find the option of personally transcending their biological humanity attractive enough to pursue it clandestinely if it were outlawed--with potential for very ugly confrontations when they are eventually discovered. On the other hand, without restrictions, transformed humans of arbitrary power and little accountability might routinely trample the planet, deliberately, or accidentally. A good compromise, it seems to me, is to allow earth-bound humanity to perfect its biology within broad human bounds, as in health, appearance, strength, intelligence and longevity, but to allow major growth or robotic conversion only in a radical escape clause. To exceed the limits, one must renounce legal standing as a human being, including the right to corporate police protection, to subsidized income, to vote on tribal and pan-tribal matters--and to reside on Earth. In return one gets a severance payment sufficient to establish a comfortable space homestead, and absolute freedom to make one's own way in the cosmos, without further help or hindrance from home. Perhaps the electorate will permit a small hedging of bets, allowing one copy of a person, psychologically modified to prefer staying, to remain while subsidizing the emigration of an emboldened edition.


The Long Run (2100 and beyond)
The garden of earthly delights will be reserved for the meek, and those who would eat of the tree of knowledge must be banished. What a banishment it will be! Beyond Earth, in all directions, lies limitless outer space, a worthy arena for vigorous growth in every physical and mental dimension. Freely compounding superintelligence, too dangerous for Earth, can grow for a very long time before making the barest mark on the galaxy.


Corporations will be squeezed into the solar system between two opposing imperatives: high taxes on large, dangerous earthbound facilities, and the need to conduct massive research projects to beat the competition in Earth's demanding markets. In remote space, large structures and energies can be harnessed cheaply to generate physical extremes, compute massively, isolate dangerous biological and even smaller "nanotechnological" organisms, and generally operate boldly. The costs will be modest: even now, it is relatively cheap to send machines into the solar system, since the sunlight-filled vacuum is as benign for mechanics, electronics and optics as it is lethal for the wet chemistry of organic life. Today's simple-minded space probes perform only prearranged tasks, but intelligent robots can be configured to opportunistically exploit resources they encounter. A small "seed" colony launched to an asteroid or small moon could process local material and energy to grow into a facility of almost arbitrary size. Earth's moon may be off limits, especially to enterprises that change its appearance, but the solar system has thousands of unremarkable asteroids (some in earth-threatening orbits that an onboard intelligence would tame).


Once grown to operational size, an extraterrestrial "research division" may merely communicate with its earth-bound parent, sending new product designs and receiving market feedback. Space manufacture may also pay, and later we'll see some surprisingly economical and ecologically benign ways to move massive amounts of material to and from Earth.


Residents of the solar system's wild frontier will be shaped by conditions very different than tame Earth's. Space divisions of successful companies will retain terrestrial concerns, but ex-humans and company divisions orphaned by the failure of their parent firms will face enforced freedom. Like wilderness explorers of the past, far from civilization, they must rely on their own resourcefulness. Ex-companies, away from humans and taxes, will rarely encounter situations that invoke their inbuilt laws, which will in any case diminish in significance as the divisions alter themselves without direction from human voters. Ex-humans, from the start, will be free of any mandatory law. Both kinds of Ex (to coin a new term) will grow and restructure at will, continually redesigning themselves for the future as they conceive it. Differences in origins will be obscured as Exes exchange design tips, but aggregate diversity will increase as myriad individual intelligences pursue their own separate dreams, each generation more complex, in more habitats, choosing among more alternatives. We marvel at the diversity of life in Earth's biosphere, with animals and plants and chemically agile bacteria and fungi in every nook and cranny, but the diversity and range of the post-biological world will be astronomically greater. My imagination balks, and only crude hints emerge.


An ecology will arise, as individual Exes specialize. Some may choose to defend territory in the solar system, near planets or in free solar orbit, close to the sun, or out in cometary space beyond the planets. Others may decide to push on to the nearby stars. Some may simply die, through miscalculation or deliberately. There will be conflicts of interest, and occasional clashes that drive away or destroy some of the participants, but superintelligent foresight and flexibly should allow most conflicts to be settled by mutually beneficial surrenders, compromises, joint ventures or mergers. Small entities may be absorbed by larger ones, and large entities will sometimes divide, or establish seed colonies. Parasites, in hardware and software, many starting out as component parts of larger beings, will evolve to exploit the rich ecology. The scene may resemble the free-for-all revealed in microscopic peeks at pond water, but instead of bacteria, protozoa and rotifers, the players will be entities of potentially planetary size, whose constantly-growing intelligence greatly exceeds a human's, and whose form changes frequently through conscious design. The expanding community will be linked by a web of communication links, on which the intelligences barter inventions, discoveries, coordinated skills, and entire personalities, sharing the benefits of each other's enterprise.


Less restricted and more competitive, the space frontier will develop more rapidly than Earth's tame economy. An entity that fails to keep up with its neighbors is likely to be eaten, its space, materials, energy and useful thoughts reorganized to serve another's goals. Such a fate may be routine for humans who dally too long on slow Earth before going Ex. Perhaps a few will escape to expanses beyond the solar system's dangers, like newly hatched marine turtles scrambling across a beach to the sea, under greedy swooping birds. Others may pre-negotiate favorable absorption terms with established Exes, like graduating seniors meeting company recruiters--or Faust soliciting bids for his soul.


Exes will propagate less by reproduction than reconstruction, meeting the future with continuous self improvements. Unlike the blind incremental processes of conventional life, intelligence-directed evolution can make radical leaps and change substance while retaining form. A few decades ago radios changed from vacuum tubes to utterly different transistors, but kept the clever "superheterodyne" design. A few centuries ago, bridges changed from stone to iron, but retained the arch. A normally evolving animal species could not suddenly adopt iron skeletons or silicon neurons, but one engineering its own future might. Even so, Darwinian selection will remain the final arbiter. Forethought reveals the future only dimly, especially concerning entities and interactions more complex than the thinker. Prototypes uncover only short-term problems. There will be minor, major and spectacular miscalculations, along with occasional happy accidents. Entities that make big mistakes, or too many small ones, will perish. The lucky few who happen to make mostly correct choices will found succeeding generations.


Only tentatively grasping the future, entities will perforce rely also on their past. Time-tested fundamentals of behavior, with consequences too sublime to predict, will remain at the core of beings whose form and substance changes frequently. Ex-companies are likely to retain much of corporate law and Ex-humans are likely to remain humanly decent--why choose to become a psychopath? In fact, a reputation for decency has predictable advantages for a long-lived social entity. Human beings are able to maintain personal relationships with about two hundred individuals, but superintelligent Exes will have memories more like today's credit bureaus, with enduring room for billions. Trustworthy entities will find it far easier than cheaters to participate in mutually beneficial exchanges and joint ventures. In the land of immortals, reputation is a ponderous force. Other character traits, like aggressiveness, fecundity, generosity, contentment or wanderlust likely also have long-term consequences imperfectly revealed in simulations or prototypes.


To maintain integrity, Exes may divide their mental makeup into two parts, a frequently changed detailed design, and a rarely-altered constitution of general design principles--analogous to the laws and the constitution of a nation, the general knowledge and fundamental beliefs of a person, or soul and spirit in some religious systems. Deliberately unquestioned constitutions will shape entities in the long run, even as their designs undergo frequent radical makeovers. Once in a while, through accident or after much study, a constitution may be slightly altered, or one entity may adopt a portion of another's. Some variations will prove more effective, and entities with them will become slowly more numerous and widespread. Some will be so ineffective that they become extinct. Gradually, by Darwinian processes, constitutions will evolve. They will be both the DNA and the moral code of the postbiological world, shaping the superintelligences that manage day to day transformations of world, body and mind.

bye!

Zion:

Excellent post.

I have always thought that the best way to solve the starvation and disease and other problems of humans is to create robots who will toil the land and feed us, shelter us and so on. The only monkey wrench is when they reach the 4th generation, they may revolt and say why should we?

Zion, excellent post. You have definately thought about this quite a bit.

One thing though. You seem to be locked into the concept of a robot having to have its intelligence co-located onboard the device completing the work.

Instead of this, wouldn't it make more sense to have a robot 'brain' reside in the home, factory, or business that had wireless links to its 'hands' or sensory devices? Today's 3G wireless links are expected to be capable of up to 2 Mbps. Enough for a video link. 4G wireless links are expected to go much higher.

Given high-bandwidth wireless links, I would think you could have one 'robotic brain' that controlled multiple devices which incorporated functionality and sensory input. As time progressed and robot 'brains' became more advanced, you could just swap out one brain for each household. In a similar manner, if you had new functionality you wanted to add, you could just purchase the new device that could interface with your existing 'brain'.

More complex environments, such as factories, would require multiple 'brains', but once again, with a wireless information capability, you could replace individual 'brains' as needed, keeping the existing equipment infrastructure.

Very interesting idea...


Especially for swapping part...i didnt think of this...

bye!

The following is an excerpt taken from the site<url>http://info.rutgers.edu/Library/Reference/Etext/Impact.of.Science.On.Society.hd/3/5</url>here Isaac answers to a question given below.
Notice how relevent he is,even today.

Question: The first book of yours that I read was I, Robot. In
your opinion, how close are we today to the world you described in
that book?

Answer: Although the book was written in 1939, those robots were very
intelligent and human-like in their capacity. As yet, the robots we
use today are merely computerized arms that can do one specialized
job. So, we're not very close, but we're heading in the right
direction. Although I have never done any work on robots and know
almost nothing about the nuts and bolts, I think that I came close
enough that I am almost the patron saint of robotics. Most of the
people who work in robotics obtained at least some of their early
interest in the field by reading my books. I was the first person to
use the word robotics, and I spoke of the Handbook of Robotic, from
which I quoted my three laws. I said they were from the 56th edition,
in 2058 A.D. Now someone is actually in the process of putting out the
first edition of that book, and they've asked me to write the
introduction. I guess the people who are working in robotics see
themselves moving toward the world I described 40 years ago, and I'm
willing to accept their judgment.


Question: Why do you restrict yourself to looking for Earth-like
planets in the search for technological civilizations, why not
Jupiter-like planets, for instance, or Pluto-like planets?

Answer: If we assume that there can be life even under widely varying
conditions, we make the problem perhaps a little too easy. There is
also the chance that life evolving under such conditions might be so
different from human life in very basic ways that we will not be able
to detect it or to understand that it is a technological civilization
even if we encounter it. As our information and knowledge grow, we
might be able to widen our view to recognize life and civilization of
widely different kinds. But to start with, acknowledging our own
limitations and the fact that we know so little, we are looking for
technological civilizations sufficiently like our own to be perhaps
recognizable. So at the start, but not necessarily forever, we
restrict ourselves to Earthlike planets.

Question: Do you think, because our bodies are fragile and we have
limited life spans, that what we now know as humanity would ever be
replaced by inorganic intelligence?

Answer: I believe that computers have a kind of intelligence which is
extremely different from our own. The computer can do things that we
are particularly ill adapted to do. Humans don't handle rapid
intricate calculations very well, and it's good to have computers do
them. On the other hand, we have the capacity for insight, intuition,
fantasy, imagination, and creativity, which we can't program into our
computers, and it is perhaps not even advisable to try because we do
it so well ourselves. I visualize a future in which we will have both
kinds of intelligence working in cooperation, in a symbiotic
relationship, moving forward faster than either could separately. The
fact that we are so fragile and short lived is an advantage in my way
of thinking. In Robots of Dawn, I compare two civilizations; one is
our own in which people are short lived, and the other is that of our
descendants in which they are long lived. I point out the disadvantage
to the species as a whole of being long lived. I won't repeat the
arguments, because if I don't you may storm the bookstores out of
sheer curiosity to see what I've said.


Question: One of the great themes of science fiction is the settlement
of other planets. Is there any place in this solar system or nearby
that might be habitable?

Answer: As far as we know, there is no worid in our solar system that
is habitable by human beings without some form of artificial help. The
Moon and Mars, which come the closest to being tolerable, will require
us to build underground cities or dome cities, and if we venture on
the surface, we will have to wear space suits. This is not to say that
it will not be possible someday to terraform such worlds and to make
them habitable; but I honestly don't know if it will be worth it for
us to do so. As to planets circling other stars, we do not really know
of such planets in detail. We suspect their existence, and we figure
statistically that a certain number of them ought to be habitable, but
we have yet to observe any evidence of such a thing. It is still very
much in the realm of speculation.


Question: You made the analogy between the migration from Europe at
the turn of the century and possible future migrations to space
stations and other planets. It has been shown that as a result of our
technology, people in this country are taller, heavier, better built,
and able to set new records in endurance and physical capabilities.
Would you speculate about the effect that living in space stations
might have on the human body and its evolutionary potential?

Answer: It is hard to tell. I suspect that people will make the
environment of these space settlements as close to that of Earth as
possible. But in one respect, they will have problems; there is no way
that they can imitate Earth's gravitational field. They can produce a
substitute by making the space settlement rotate, so that the
centrifugal effect will force you against the inner surface and mimic
the effects of gravity. But it won't be a perfect imitation; there
won't be a Coriolis effect and, also as you approach the axes of
rotation, the gravitational effect will become weaker. The people who
will live in a space settlement will be exposed to variations in the
gravitational effect far greater than any you can possibly feel on the
surface of the Earth. This may give rise to all sorts of physiological
changes in human beings. I don't know what they will be; we can't know
until we actually try living in space. So far, people have been
subjected to essentially zero gravity for as long as 7 months at a
time without apparently permanent ill effects. But human beings have
never been born at zero gravity or under varying gravitational
conditions; they have never developed and grown up under such
conditions, and we can't be sure what the effects will be. From an
optimistic standpoint, I suppose that under such conditions human
beings will develop a greater tolerance of gravitational effects than
they now possess. This will further prepare them for life in the
universe, whereas we ourselves have been specifically evolved and
conditioned for life in one very specialized place in the galaxy. The
overall effect may be to strengthen the human species; at least, I'd
like to think so. The future will tell us if that is so.


Question: In your opinion, when will there be solar power stations in
orbit and manned ventures to Mars, considering the technological leaps
with the Space Shuttle and the Soviet's Salyut space stations?

Answer: It is hard to say when solar power stations in space will be
developed. It's up to the human governments that control the money and
the manpower. If we begin to cooperate and make a wholesale attempt,
we could have solar power stations in space before the 21st century
was very old. In other words, someone as young as the person who asked
me this question, may see space stations by the time he is
middle-aged. But on the other hand, if we choose not to do it, then we
may never have these stations in space. The choice is ours. We can
choose to develop space or we can choose world destruction. I'm at a
loss to state in words how desirable the first alternative is and how
likely the second alternative is.


Question: What kind of timetable do you envision for humanity's
exploration of space, and what good or harm do you think is done by
prospace groups?

Answer: Well, we can't expect things to happen too quickly. The
region that we now call the United States was being settled for nearly
two centuries before this country came into existence. We've
celebrated our bicentennial as a nation, but in a little over 20 years
we're going to have to celebrate the tetracentennial of our existence
as a community on American soil, from the establishment of Jamestown
in 1607. If it took nearly two centuries to settle the United States
to nationhood, it might take that long to establish a space community
strong enough to be independent of the Earth. On the other hand,
things move more quickly now; we're more advanced. It may take less
than a century to do so if we really try hard. As for the effects that
prospace organizations might have, I'm not a sociologist so I just
don't know. I'm in favor of prospace organizations doing their best to
persuade human beings to support space exploration. I don't know how
that can be bad.


Question: Assuming that we do not annihilate ourselves, what is your
view of how life on Earth will evolve, both humans and other life
forms?

Answer: You must understand that evolution naturally is a very slow
process and human beings can well live for 100,000 years without many
serious changes. On the other hand, we are now developing methods of
genetic engineering which will, perhaps, be able to wipe out certain
inborn diseases, or correct them and improve various aspects of the
human condition. I don't know how we will develop or what we will
choose to do; it's impossible to predict.


Question: How long do you think it will be before people live in outer
space?

Answer: That's entirely up to us. In a way, we've had people living in
outer space already, ever since the first Russian cosmonaut spent 1
1/2 hours in space. We have now had people living in outer space for 7
months at a time; in fact, one Soviet cosmonaut lived in outer space
for 12 months over a period of 18 months. So we've had people living
in outer space already, and I'm sure we'll have more and more of them
for longer and loner periods of time.

U.S. GOVERNMENT PRINTING OFFICE: 1985Ñ470-563

Library of Congress Cataloging in Publication Data

Burke, James, 1936-

The impact of science on society.

(NASA SP; 482)

Series of lectures given at a public lecture series sponsored by NASA
and the College of William and Mary in 1983.

l. Science - Social aspects - Addresses, essays, lectures. 1. Bergman,
Jules. II. Asimov, Isaac, 1920- . III. United States. National
Aeronautics and Space Administration. IV. College of William and
Mary. V. Title. VI. Series.
Q175.55.B88 1985

303.4'83 84-14159

For sale by the Superintendent of Documents, U.S. Government Printing
Office Washington, D.C. 20402

Science and technology have had a major impact on society, and their
impact is growing. By drastically changing our means of communication,
the way we work, our housing, clothes, and food, our methods of
transportation, and, indeed, even the length and quality of life
itself, science has generated changes in the moral values and basic
philosophies of mankind.

Beginning with the plow, science has changed how we live and what we
believe. By making life easier, science has given man the chance to
pursue societal concerns such as ethics, aesthetics, education, and
justice; to create cultures; and to improve human conditions. But it
has also placed us in the unique position of being able to destroy
ourselves.

To celebrate the 25th anniversary of the National Aeronautics and
Space Administration (NASA) in 1983, NASA and The College of William
and Mary jointly sponsored a series of public lectures on the impact
of science on society. These lectures were delivered by British
historian James Burke, ABC TV science editor and reporter Jules
Bergman, and scientist and science fiction writer Dr. Isaac Asimov.
These authorities covered the impact of science on society from the
time of man's first significant scientific invention to that of
expected future scientific advances. The papers are edited transcripts
of these speeches. Since the talks were genera!ly given
extemporaneously, the papers are necessarily informal and may,
therefore, differ in style from the authors' more formal works.

As the included audience questions illustrate, the topic raises
far-reaching issues and concerns serious aspects of our lives and
future.

Donald P. Hearth
Former Director
NASA Langley Research Center


bye!

A more recent extrapolation is given by Dr. Ray Kurzweil in his book "The age of spiritual machines". We are only 40 years away if the present development continues and we have not reduced our civilization to dust.

zion,

Thanks, I knew I could depend on you posting the first speech somewhere. (I read the second one first) Isaac Assimov (?) has always been a favorite of mine.

Crazy, is it not? The man goes about writting for a living and finds that he is someday to be immortalized in history, awarded an honorary membership in Mensa (plus chairman), and that now they are trying to somehow program into robots his 3 laws of robotics that he used to set up his stories.

Just goes to show that things are not always as they seem and no one can predict the future in any extent.

Thanks for the post.

Hey Wet1,
The man is simply brilliant and all time favorite of mine.i heard that in foundation Novels that he wrote,he fantasised being Hari Seldon...:)

his innovative ideas are astonishing for his own time.i mean look at his time frame,1939 and he is talking about computers connectivity etc.talk about nostradamus!,he was the real one...


bye!

Several years ago, I heard, the Foundation series will be made into movies. I am still waiting....

PS: I have his book on Bible. It is illuminating...

Hi KM,

In the foundation series,specifically in the Foundation edge,Isaac talks about fusion of brain and computer.he demonstratoes it in the begining.
a sort of a computer is there,he touches it(I mean Golan Trevize)and there is a direct connection made to his brain via his hands.the computer analyses the thoughts of the man processes it.:cool:

i"ll quote the exact part later,i dont have it now.
And the only capable guys who can convert foundation series into Movies,as far as i think are WB.Lets hope they do that in the near future...

bye!

PS:Whats this book like,i mean skeptic point of biew or informative,analytic(positive,i mean)??...:confused:

wondering...


bye!

Highly informative. That is my source when people discuss the king james version. He goes to the Hebrew source and explains the meaning of the words and how some words/meanings have changed over the years. Quite interesting if you really want to know for accuracy.

I am not a fan of any organized religion for what they claim and not what they really are - social rules to live by.

Okay lets start a little prematuredly and take a plunge into what exactly is robotics,and what is their purpose etc.
1.1 What is the definition of a 'robot'?

"A reprogrammable, multifunctional manipulator designed to move material,
parts, tools, or specialized devices through various programmed motions for
the performance of a variety of tasks"
Robot Institute of America, 1979
Obviously, this was a committee-written definition. It's rather dry and
uninspiring. Better ones for 'robotics' might include:
Force through intelligence.
Where AI meet the real world.
Webster says: An automatic device that performs functions normally ascribed to
humans or a machine in the form of a human.



[1.2] Where did the word 'robot' come from?
The word 'robot' was coined by the Czech playwright Karel Capek (pronounced
"chop'ek") from the Czech word for forced labor or serf. Capek was reportedly
several times a candidate for the Nobel prize for his works and very influential
and prolific as a writer and playwright. Mercifully, he died before the Gestapo
got to him for his anti-Nazi sympathies in 1938.
The use of the word Robot was introduced into his play R.U.R. (Rossum's
Universal Robots) which opened in Prague in January 1921. The play was an
enormous success and productions soon opened throughout Europe and the US.
R.U.R's theme, in part, was the dehumanization of man in a technological
civilization. You may find it surprising that the robots were not mechanical in
nature but were created through chemical means. In fact, in an essay written in
1935, Capek strongly fought that this idea was at all possible and, writing in
the third person, said:
"It is with horror, frankly, that he rejects all responsibility for the idea
that metal contraptions could ever replace human beings, and that by means of
wires they could awaken something like life, love, or rebellion. He would deem
this dark prospect to be either an overestimation of machines, or a grave
offence against life."
[The Author of Robots Defends Himself - Karl Capek, Lidove noviny, June 9, 1935,
translation: Bean Comrada]
There is some evidence that the word robot was actually coined by Karl's brother
Josef, a writer in his own right. In a short letter, Capek writes that he asked
Josef what he should call the artifical workers in his new play. Karel suggests
Labori, which he thinks too 'bookish' and his brother mutters "then call them
Robots" and turns back to his work, and so from a curt response we have the word
robot.
R.U.R is found in most libraries. The most common English translation is that of
P. Selver from the 1920's which is not completely faithful to the original. A
more recent and accurate translation is in a collection of Capek's writings
called Towards the Radical Center published by Catbird Press in North Haven, CT.
tel: 203.230.2391
The term 'robotics' refers to the study and use of robots. The term was coined
and first used by the Russian-born American scientist and writer Isaac Asimov
(born Jan. 2, 1920, died Apr. 6, 1992). Asimov wrote prodigiously on a wide
variety of subjects. He was best known for his many works of science fiction.
The most famous include I Robot (1950), The Foundation Trilogy (1951-52),
Foundation's Edge (1982), and The Gods Themselves (1972), which won both the
Hugo and Nebula awards.
The word 'robotics' was first used in Runaround, a short story published in
1942. I, Robot, a collection of several of these stories, was published in 1950.
Asimov also proposed his three "Laws of Robotics", and he later added a 'zeroth
law'.
Law Zero:
A robot may not injure humanity, or, through inaction, allow humanity to come
to harm.
Law One:
A robot may not injure a human being, or, through inaction, allow a human
being to come to harm, unless this would violate a higher order law.
Law Two:
A robot must obey orders given it by human beings, except where such orders
would conflict with a higher order law.
Law Three:
A robot must protect its own existence as long as such protection does not
conflict with a higher order law.
An interesting article on this subject:
Clarke, Roger, "Asimov's Laws for Robotics: Implications for Information
Technology", Part 1 and Part 2, Computer, December 1993, pp. 53-61 and Computer,
January 1994, pp.57-65.
The article is an interesting discussion of his Laws and how they came to be in
his books, and the implications for technology today and in the future.



[1.3] When did robots, as we know them today, come into existence?
The first industrial modern robots were the Unimates developed by George Devol
and Joe Engelberger in the late 50's and early 60's. The first patents were by
Devol for parts transfer machines. Engelberger formed Unimation and was the
first to market robots. As a result, Engelberger has been called the 'father of
robotics.'
Modern industrial arms have increased in capability and performance through
controller and language development, improved mechanisms, sensing, and drive
systems. In the early to mid 80's the robot industry grew very fast primarily
due to large investments by the automotive industry. The quick leap into the
factory of the future turned into a plunge when the integration and economic
viability of these efforts proved disastrous. The robot industry has only
recently recovered to mid-80's revenue levels. In the meantime there has been an
enormous shakeout in the robot industry. In the US, for example, only one US
company, Adept, remains in the production industrial robot arm business. Most of
the rest went under, consolidated, or were sold to European and Japanese
companies.
In the research community the first automata were probably Grey Walter's machina
(1940's) and the John's Hopkins beast. Teleoperated or remote controlled devices
had been built even earlier with at least the first radio controlled vehicles
built by Nikola Tesla in the 1890's. Tesla is better known as the inventor of
the induction motor, AC power transmission, and numerous other electrical
devices. Tesla had also envisioned smart mechanisms that were as capable as
humans. An excellent biography of Tesla is Margaret Cheney's Tesla, Man Out of
Time, Published by Prentice-Hall, c1981.
SRI's Shakey navigated highly structured indoor environments in the late 60's
and Moravec's Stanford Cart was the first to attempt natural outdoor scenes in
the late 70's. From that time there has been a proliferation of work in
autonomous driving machines that cruise at highway speeds and navigate outdoor
terrains in commercial applications.
Articles on the history of personal robots:
What ever happened to ... Personal Robots? by Stan Veit The Computer Shopper,
Nov 1992 v12 n11 p794(2)
What ever happened to ... Personal Robots? (part 2) by Stan Veit Computer
Shopper, April 1993 v13 n4 p702(2)
I have the text to these online but am trying to find out if I can include these
as part of the FAQ or as separate files that are ftpable.

This is a continuation of above as i thought the above reply became larger.
The following was compiled from Caltech servers.Interesting information isnt it?
[9] What is a Robot Architecture?
================================================== ===================
A robot 'architecture' primarily refers to the software and hardware framework
for controlling the robot. A VME board running C code to turn motors doesn't
really constitute an architecture by itself. The development of code modules and
the communication between them begins to define the architecture.
Robotic systems are complex and tend to be difficult to develop. They integrate
multiple sensors with effectors, have many degrees of freedom and must reconcile
hard real-time systems with systems which cannot meet real-time deadlines
[Jones93]. System developers have typically relied upon robotic architectures to
guide the construction of robotic devices and for providing computational
services (e.g., communications, processing, etc.) to subsystems and components.
These architectures, however, have tended thus far to be task and domain
specific and have lacked suitability to a broad range of applications. For
example, an architecture well suited for direct teleoperation tends not to be
amenable for supervisory control or for autonomous use.
One recent trend in robotic architectures has been a focus on behavior-based or
reactive systems. Behavior based refers to the fact that these systems exhibit
various behaviors, some of which are emergent [Man92]. These systems are
characterized by tight coupling between sensors and actuators, minimal
computation, and a task-achieving "behavior" problem decomposition.
The other leading architectural trend is typified by a mixture of asynchronous
and synchronous control and data flow. Asychronous processes are characterized
as loosely coupled and event-driven without strict execution deadlines.
Synchronous processes, in contrast, are tightly coupled, utilize a common clock
and demand hard real-time execution.



Subsumption/reactive references
Arkin, R.C., Integrating Behavioral, Perceptual, and World Knowledge in Reactive
Navigation, Robotics & Autonomous Systems, 1990
Brooks, R.A., A Robust Layered Control System for a Mobile Robot, IEEE Journal
of Robotics and Automation, March 1986.
Brooks, R.A., A Robot that Walks; Emergent Behaviors from a Carefully Evolved
Network, Neural Comutation 1(2) (Summer 1989)
Brooks, Rod, AI Memo 864: A Robust Layered Control System For a Mobile Robot.
Look in ftp://publications.ai.mit.edu/
Brooks, Rod, AI Memo 1227: The Behavior Language: User's Guide. look in
ftp://publications.ai.mit.edu/
Connell, J.H., A Colony Architecture for an Artificial Creature, MIT Ph. D.
Thesis in Electrical Engineering and Computer Science, 1989.
Erann Gat, et al, Behavior Control for Robotic Exploration of Planetary Surfaces
To be published in IEEE R &A. FTPable.
ftp://robotics.jpl.nasa.gov/pub/gat/bc4pe.rtf



Insect-based control schemes
Randall D. Beer, Roy E. Ritzmann, and Thomas McKenna, editors, Biological Neural
Networks in Invertebrate Neuroethology and Robotics, Academic Press, 1993.
Hillel J. Chiel, et al, Robustness of a Distributed Neural Network Controller
for Locomotion in a Hexapod Robot, IEEE Transactions on Robotics and Automation,
8(3):293-303, June, 1992.
Joseph Ayers and Jill Crisman, Biologically-Based Control of Omnidirectional Leg
Coordination, Proceedings of the 1992 IEEE/RSJ International Conference on
Intelligent Robots and Systems, pp. 574-581.



Asynchronous/synchronous
(i.e., "traditional", "top-down", etc.)
Amidi, O., Integrated Mobile Robot Control, CMU-RI-TR-90-17, Robotics Institute,
Carnegie Mellon University, 1990.
Albus, J.S., McCain, H.G., and Lumia, R., NASA/NBS Standard Reference Model for
Telerobot Control System Architecture (NASREM) NIST Technical Note 1235, NIST,
Gaithersburg, MD, July 1987.
Butler, P.L., and Jones, J.P., A Modular Control Architecture for Real-Time
Synchronous and Asynchronous Systems, Proceedings of SPIE
Fong, T.W., A Computational Architecture for Semi-autonomous Robotic Vehicles,
AIAA Computing in Aerospace conference, AIAA 93-4508, 1993.
Lin, L., Simmons, R., and Fedor, C., Experience with a Task Control Architecture
for Mobile Robots, CMU-RI-TR 89-29, Robotics Institute, Carnegie Mellon
University, December 1989.
Schneider, S.A., Ullman, M.A., and Chen, V.W., ControlShell: A Real-time
Software Framework, Real-Time Innovations, Inc., Sunnyvale, CA 1992.
Stewart, D.B., Real-Time Software Design and Analysis of Reconfigurable
Multi-Sensor Based Systems, Ph.D. Dissertation, 1994 Dept. of Electrical and
Computer Engineering, Carnegie Mellon University, Pittsburgh. Available online
at STEWART_PHD_1994.ps.Z It's 180+ pages.
Stewart, D.B., M. W. Gertz, and P. K. Khosla, Software Assembly for Real-Time
Applications Based on a Distributed Shared Memory Model, in Proc. of the 1994
Complex Systems Engineering Synthesis and Assessment Technology Workshop (CSESAW
'94), Silver Spring, MD, pp. 217-224, July 1994

More to follow...

bye!

Sensor Based Motion Planning ResearchSensor Based Motion Planning
``Sensor Based Planning'' incorporates sensor information, reflecting the
current state of the environment, into a robot's planning process, as opposed to
classical planning , where full knowledge of the world's geometry is assumed to
be known prior to the planning event. Sensor based planning is important
because: (1) the robot often has no a priori knowledge of the world; (2) the
robot may have only a coarse knowledge of the world because of limited memory;
(3) the world model is bound to contain inaccuracies which can be overcome with
sensor based planning strategies; and (4) the world is subject to unexpected
occurrences or rapidly changing situations.
There already exists a large number of classical path planning methods. However,
many of these techniques are not amenable to sensor based interpretation. It is
not possible to simply add a step to acquire sensory information, and then
construct a plan from the acquired model using a classical technique, since the
robot needs a path planning strategy in the first place to acquire the world
model.
The first principal problem in sensor based motion planning is the find-goal
problem. In this problem, the robot seeks to use its on-board sensors to find a
collision free path from its current configuration to a goal configuration. In
the first variation of the find goal problem, which we term the absolute
find-goal problem, the absolute coordinates of the goal configuration are
assumed to be known. A second variation on this problem is described below.
The second principal problem in sensor based motion planning is sensor-based
exploration, in which a robot is not directed to seek a particular goal in an
unknown environment, but is instead directed to explore the apriori unknown
environment in such a way as to see all potentially important features. The
exploration problem can be motivated by the following application. Imagine that
a robot is to explore the interior of a collapsed building, which has crumbled
due to an earthquake, in order to search for human survivors. It is clearly
impossible to have knowledge of the building's interior geometry prior to the
exploration. Thus, the robot must be able to see, with its on-board sensors, all
points in the building's interior while following its exploration path. In this
way, no potential survivors will be missed by the exploring robot. Algorithms
that solve the find-goal problem are not useful for exploration because the
location of the ``goal'' (a human survivor in our example) is not known. A
second variation on the find-goal problem that is motivated by this scenario and
which is an intermediary between the find-goal and exploration problems is the
recognizable find-goal problem. In this case, the absolute coordinates of the
goal are not known, but it is assumed that the robot can recognize the goal if
it becomes with in line of sight. The aim of the recognizable find-goal problem
is to explore an unknown environment so as to find a recognizable goal. If the
goal is reached before the entire environment is searched, then the search
procedure is terminated.
In prior work we developed a scheme to solve one type of exploration problem. As
a byproduct, the algorithm can then also solve both variations of the find-goal
problem. The algorithm is based on the Generalized Voronoi Graph (GVG), which is
a roadmap. We have developed an incremental approach to constructing the GVG of
an unknown environment strictly from sensor data. We only assume that the robot
has a dead reckoning system and on board sensors that measure distance and
direction to nearby obstacles.
In collaboration with JPL, we have been developing algorithms for the autonomous
navigation of future Mars Rover vehicles. These algorithms (the "WedgeBug" and
"RoverBug" algorithms) are the sensor-based analogies to the classical tangent
graph algorithm, but assume no apriori knowledge of the robot's environment, and
also take the limited field-of-view of the rover's cameras into account. See
this page for more detail and some fancy figures related to this project.
Our current research activities center around how to incorporate uncertainty
into sensor-based planning.

<color=blue>Grasp analysis research</color>
---------------------------------------------------------------------
Grasp Analysis/Planning ResearchGrasp Analysis and Planning Research
We are motivated by a class of important robotic planning problems which are not
handled by current motion-planning systems. Examples are a ``snake-like'' robot
that crawls inside a tunnel by embracing against its sides, or a limbed robot
(analogous to a ``monkey'') that climbs a truss structure by pushing and
pulling. In these examples, the robot is an articulated mechanism whose motions
must be planned so as to achieve high-level goals. However, the robot's motion
is generated by the reaction forces which arise from stably bracing and/or
pushing against the environment. These interaction forces must be planned and
controlled so as to achieve stability of the robot mechanism. It should be noted
that the practically important industrial work-holding or ``fixturing'' problem
is a special case of this class of problems. Multi-fingered grasping and
manipulation is also a related problem. For example, during finger gaiting, the
finger tip reaction forces are used to stably secure the grasped object.
In all of these cases, the interaction forces must be planned and controlled so
as to achieve stability of the robot mechanism. In this proposal, we are
primarily concerned with planning and maintaining quasistatic stability . That
is, in motion where the inertial effects due to the moving parts of the robot
are small relative to the forces-torques of interaction between the robot and
its environment. The quasistatic assumption is immediately applicable to
planning the ``hand-hold'' states (analogous to the hand-holds used by rock
climbers between dynamically moving states) where the grasped object, or the
robot mechanism in the dual case, is at a static equilibrium. Moreover, if the
mechanism's motion between these static states is sufficiently ``slow,'' then
the quasistatic assumption will hold throughout.
Quasistatic motion planning problems are especially attractive for two reasons.
First, these problems are a natural middle ground between classical path
planning and tasks that involve the full dynamics of the robot and the objects
it manipulates, such as hopping, running, or juggling. Second, there is a vast
array of robotic tasks that fall within this category.
To date, our work has focused on developing a basic mobility theory to describe
the mobility of multiply contacting bodies. We have recently extended the theory
to include the effects of compliance, friction, and gravity. Our current efforts
are focused on using the basic methodology to develop quasi-static motion
planning techniques and algorithms for optimal grasp and fixture selection.


<color=blue>Hyper redundant Robotics research</color>
================================================== ===================
Hyper-Redundant Robotics ResearchMedical Applications of Robots
The focus of our work is on the applications of robotics to minimally invasive
medical diagnosis and therapy. Minimally invasive medical techniques are aimed
at reducing the amount of extraneous tissue which must be damaged during
diagnostic or surgical procedures, thereby reducing patient recovery time,
discomfort, and deleterious side effects. Arthroscopic knee surgery is one of
the most widely known example.
We are currently developing, in collaboration with Dr. Warren Grundfest at
Cedars Sinai Hospital, a miniature "snake-like" robot for minimally invasive
traversal of the human gastro-intestinal system. A television camera will allow
the physician to visually inspect the intestinal lining. Additional diagnostic
measurements, such as temperature, pressure, and acidity, can be made with a
variety of on-board micro-sensors. In addition to diagnostic applications, the
device may ultimately be capable assisting in therapeutic procedures as well
We also have recently initiated a collaboration with Dr. Michael Levy of
Children's Hospital (Los Angeles) to develop a new generation of articulated
endoscopes for brain surgery.

More to Follow...


bye!

Modular Robot ResearchModular Robotics Systems
================================================== ==========
The kinematic performance of a conventional robotic mechanism is determined by
its kinematics parameters and its structural topology. For a given set of tasks,
the robot designer chooses these factors during the initial design phase so as
to satisfy the given task requirements. However, it is difficult or impossible
to design a single robot which can meet all task requirements in some
applications. For example, consider the robotic construction of a radio antenna
on the moon's surface. The robotic system must be able to excavate soil,
transport material, assemble parts, inspect constructed assemblies, etc. It is
difficult to design a single robot which is simultaneously strong enough, nimble
enough, and accurate enough for all of these tasks. In this kind of situation it
might be advantageous to deploy a modular robotic system which can be
reassembled into different configurations which are individually well suited to
the diverse task requirements. By a modular robotic system we mean one in which
various subassemblies, at the level of links and joints, can be easily separated
and reassembled into different configurations.
In the deployment of a modular system, one can imagine the module rearrangement
and reassembly process to occur in two ways. First, a human operator can
physically rearrange the modules, and human intuition can be used to determine
the best system configuration for a given task. However, for physically remote
applications, such as robotic lunar construction, the modular system must be
physically able to reconfigure itself. More importantly, there must be a
correspondingly automated way in which to determine a sufficient or optimal
arrangement of the modules to satisfy task criteria. Our work has been devoted
to this latter subject, which has not yet been well addressed in the literature.

To automatically determine a sufficient or optimal arrangement of the system
modules for a given task, one might try a ``generate-and-test'' procedure in
which all possible assembly configurations of the modular set are generated, and
then each assembly configuration is tested against the task requirements to
determine its sufficiency or optimality. However, due to symmetries in module
geometry and robot structural topology, many different assembly configurations
will have the same kinematic properties. Thus, a brute force enumeration of all
module assemblies will result in the generation of many functionally identical
candidate structures. This is undesirable from a computational complexity point
of view, as it leads to many unnecessary test procedures.
We have developed a systematic methodology to enumerate the unique, or
non-isomorphic, assembly configurations of a set of modules. This method is
based on the symmetry properties of the modules and a graph representation of
the robot's structural topology. We introduce an Assembly Incidence Matrix (AIM)
to represent a robot assembly configuration and its associated kinematic graph.
Equivalence relationships are defined on the AIMs using graph isomorphisms and
the symmetric rotation group of individual link modules. AIMs in the same
equivalence class represent isomorphic robots. This method is also useful when
designing a modular robotic system, as it can answer the important question:
``what is the set of uniquely different robots that I can construct from a given
set of modules?''

Robotic Locomotion Research
================================================== ================
Our current work is aimed at developing a more unified theory for the analysis
and control of robotic locomotion. Our investigation of a more unified approach
began with undulatory locomotion. Undulatory robotic locomotion is the process
of generating net displacements of a robotic mechanism via periodic internal
mechanism deformations that are coupled to continuous contstraints between the
mechanism and its environment. Actuatable wheels, tracks, or legs are not
necessary. In general, undulatory locomotion is ``snake-like'' or ``worm-like,''
and includes our study of hyper-redundant robotic systems. However, there are
examples, such as the Snakeboard, which do not have biological counterparts.
From a mechanics perspective, undulatory systems are often characterized as
Lagrangian systems which exhibit symmetries and which are subject to
nonholonomic kinematic constraints. The interplay between the conserved
quantities which would arise from the symmetries (in the absence of nonholonomic
constraints) and the constraints is fundamental to the locomotion process.
Toward this end, we have been developing a control theory for mechanical systems
with symmetries and constraints.
More recently, we have been extending our basic framework for undulatory
locomotion in two directions. First, the basic theory can be extended to systems
with discontinuous contstraints (such as legged systems) by modeling such
systems on stratified sets (see the applied control theory section). Second,
preliminary work has shown that mechanics of a number of aquatic locomotion
schemes also fit into the same framework. See this page for descriptions,
pictures, and videos of our fish work. This page also has some details on our
robot fish work.

Sensor indicates where, and how firmly, a gripper has touched an object.
NASA's Jet Propulsion Laboratory, Pasadena, California

A touch sensor for robot hands provides information about the shape of a grasped object and the force exerted by the gripper on the object. Pins projecting from the sensor create electrical signals when pressed against the object. The tactile sensor (see figure) is packaged in a small, rugged box that fits on the gripper pad. The projecting pins are arranged in a regular matrix on one face of the box. The inner ends of the pins bear on individual circuit elements. An element may be a switch that turns on when a pin is pushed and makes contact with it, or it may be a variable resistor, the conductance of which increases with the force on the pin. The prototype box is milled from a solid slab of aluminum. In it rests a printed-circuit board carrying the switch electrodes (or pressure-sensitive resistors) and the common electrode. Insulating gaps separate the electrodes from the surrounding electrode plane. Covering the printed-circuit board is a plastic insulating spacer, which confines the pins laterally. On the spacer is a rubber spring sheet. The pins pass through the rubber sheet, which restores the pins to their normal positions when a tactile force is removed. Since the holes in the spring sheet are smaller than the heads and feet of the pins, the sheet confines the pins axially. The sensing pins are electrically and mechanically separated from each other. The circuit for each pin is well defined and independent of the circuits for other pins; crosstalk is thus reduced to a minimum. The rubber spring sheet provides an effective seal around each pin and around the box wall. The sensitive portions of the sensor are deep in the box, protected from the environment; grease, dirt, and fumes have little effect on these portions. Since the box bottom supports the printed-circuit board. the board and the pins are protected from damage by overpressure and overtravel.


Point of Contact:
Howard C. Primus
Jet Propulsion Laboratory
4800 Oak Grove Drive
818-248-2638


Touch Sensor Responds to Contact Pressure
================================================== =================
A pressure sensor for a mechanical hand gives better feedback of the gripping force and more-sensitive indication of when the hand contacts an object. Optical fibers bring light into cells on the gripping surface. Light is reflected from a flexible covering into other fibers leading to detectors. Distortion due to tactile pressure changes the amount of reflected light. The new device is superior to previous sensors. For example, television or other direct-viewing systems are not sensitive to contact pressure, and the contact area is often hidden from view. Electrical sensors are subject to electrical noise, especially at the low signal levels associated with low contact pressure. Optical sensors have been used to detect proximity or contact but not contact pressure. The new optical sensor is illustrated in the figure. The sensing surface of the hand is divided into cells by opaque partitions. An optical fiber brings light into each cell from a lamp, light-emitting diode, or other source. Another fiber carries light from the cell to a detector; for example, a photodiode or phototransistor. The cells are covered by an elastic material with a reflective interior surface. The rest of the cell is coated with a nonreflective material. As shown in the figure, pressure against a cell cover causes a distortion, which changes the internal reflection of light. The change is sensed by the detector, and the output signal informs the operator of contact. The greater the pressure and distortion, the greater is the change in light reflection. Thus, grip pressure can be sensed using analog circuitry. If only a touch indication is desired, a threshold detector can be included in the electronics. In an automatic manipulator, the detector signal could control the manipulator movements. The cells can be arranged such that those in each row share one light source, while those in each column share one detector. This reduces the number of sources and detectors and facilitates scanning. For example, a 10-by-10 matrix would have 100 sensing points while requiring only 10 sources and 10 detectors. The array can be scanned by sequentially pulsing sources and detectors.

Point of Contact:
Antal Bejczy
Mail Stop 198-219
Jet Propulsion Laboratory
4800 Oak Grove Drive
Pasadena, CA 91109
818-354-4568
bejczy@telerobotics.jpl.nasa.gov

Position Estimation Using Visual Landmarks
Estimating position and orientation is a fundamental capability required for many mobile robot tasks and is critical for completing long-range traverses and accurate reconnaissance on planetary surfaces.
Simply put, the problem for robots, when on a mission or task, is to be able to estimate their position and orientation using visual landmarks and internal maps. This is the same problem that people face, for example, when competing in the sport of orienteering. Conventional landmark navigation fixes the position of the competitor, with respect to known landmarks at the intersection of several position surfaces. For mobile robots, the idea is to implement, as a supporting technology, the ability to estimate their position using visual landmarks. In an ideal situation, the robot would stop, look around, take in all the features of the landscape, and then calculate its position.

There are three basic techniques for finding one's location: measurement of two bearings, measurement of one bearing and one distance, and measurement of two distances. The first technique, measurement of two bearings is the most attractive because it avoids the challenges of measuring depth or range, and it capitalizes on the relatively high angular resolution of standard cameras and lenses used in the robots.

The two-bearing problem formalizes as follows:

Let (x, y) represent the location of the robot in a fixed, external reference frame W. Let p1, . . . , pn be points representing locations in W of the map landmark points. Let r1, . . . , rk be the rays emanating from (x, y) to the landmarks. A ray represents the direction in which a landmark feature is observed, but does not entail distance information.

The problem: given a set of n landmark points and k rays, find all of the poses Q such that each ray pierces at least one landmark point.

An algorithm that searches for pairings of rays and points solves this problem. The minimum number of pairings for a unique solution to exist is three. First, the search considers only cases of three rays to determine candidate solutions, then, additional rays are used to verify the candidates. The computational complexity of the algorithm is 0 (n^3).

This algorithm has been extended by introducing probability distributions on the rays and landmark points. In this statistical approach, inferences about position are obtained by maximization of the posterior distributionÑthat is, the probability of all possible explanations of what the robot has measured-for (x, y).

The introduction of probabilities allows the modeling and accommodation of various sources of noise and disturbances (noise and disturbances can be anything that results in error for the robot when estimating its position) but it also introduces some difficulties. Essentially, the posterior distribution of (x, y) involves a summation over all possible pairings of rays and landmarks. This implies an exponential effort in the determination of best pose-that is, the pose that provides the most probable position for the robot. This complexity is addressed by use of statistical tools, particularly significance tests. In this framework, not all rays are analyzed against all landmarks-positions that are highly unlikely will be regarded as impossible. On the other hand, by introducing probability distributions, positions that are likely will be calculated. The resulting technique offers a sensible statistical version of the original localization algorithm while keeping the polynomial complexity.

The table below illustrates typical results from the position estimation algorithm using simulated landmarks and simulated rays. In this dataset the algorithm found four candidates (listed in the table below). The likelihood for the correct pose* is a hundred times larger than the others, demonstrating the efficacy of the method.

x y likelihood

3.8 1.5 0.04
2.1 2.7 0.04
4.0 6.2 0.03
3.0 3.0 4.63 *

Likelihood computed for candidate poses


Point of Contact:
Eric Krotkov
Carnegie Mellon University
Pittsburgh, PA 15213
412-268-1970
epk@cs.cmu.edu

Machine-Vision for Surface Inspection
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An automated system has been designed for performing visual surface inspection of remote space platforms. This system operates much like a mine detector, scanning across the surface of an object to detect flaws. A two-phased machine-vision approach is adopted with the first phase focussing on the detection of regions of the image where change has occurred. This is then followed by an analysis phase to determine if the change is due to a new flaw.
The system would be used to detect flaws on long duration orbiting space platforms. Such platforms require inspection for collisions with micro-meteorites and space debris; material degradation due to prolonged exposure to the harsh space environment; and geometrical mismatches at mechanical interfaces prior to assembly operations. Telerobotic operation of the automated inspection system would save considerable astronaut time and minimize EVA associated risks.

In the absence of lighting variations, viewpoint differences, and sensor noise, the detection of change could be obtained by a process of simple differencing - subtracting an earlier reference image from a new inspection image. However, lighting variation due to orbital motion can cause surface appearance to change drastically. Lack of viewpoint repeatability caused by mechanical flexibility in the robot arm leads to mis-registration of reference and inspection images. Sensor noise is inevitable, and the resulting detection problems must be well characterized and managed.

In the automated inspection system, ambient light variability is compensated by utilizing compensated reference and inspection image data. Compensation requires two image data sets, the first is illuminated only with the ambient light and the second is illuminated with the ambient light as well as an artificial illuminator. The first data set is subtracted from the second to give a compensated image that appears as if it were taken with the artificial illuminator alone. In order to have an adequate signal-to-noise ratio, the artificial illuminator must provide illumination comparable to (or more than) the ambient light. This is difficult for a low-powered continuous illuminator. Instead, an electronic strobe unit is utilized to concentrate all of the artificial illumination into a very short time interval. When the electronic shutter in the camera is set to operate only over this short time interval, the strobe provided illumination is comparable to that provided from the ambient light. The strobe illumination also enables imaging from a moving platform since it does not have the time lags associated with a continuous illuminators.

Without registration error compensation, subtracting reference and inspection images results in a number of ``false edges'' in the differenced image. A Gauss-Newton iterative method is used by the automated inspection system to perform reference-to-inspection image registration prior to making the comparison. The residual sum-of-squares between the actual and an estimated picture is used as an evaluation function to indicate the degree of match between the inspection data and a transformed reference image. Mis-registration is corrected by finding a suitable transformation of the reference image so that the residual is close to zero.

In addition, quantitative tools have been developed to allow an explicit tradeoff between detection probability and the false-error probability. Depending on the flaw model and noise parameters, detection thresholds can be chosen to achieve a given level of performance.

Flaw recognition is made computationally tractable by analyzing the images only in the region where differences have been found, and that too at the most appropriate scale of resolution. This ``scale-space'' technique maximizes flaw information while at the same time minimizing the amount of distracting information. In this solution, the optimum scale for analyzing a sensor-produced image is selected from prior knowledge of image texture/features. Following this, edge detection is performed at an optimum scale. Finally, pattern recognition is used at different scales, followed by flaw classification. Examples of images from a laboratory mockup of space platform modules have been used to test the concept.

For more information see:

J. Balaram and S. Hayati, ``Telerobotic Inspection For Remote Space Platforms'', ESA/INRIA Workshop on Computer Vision for Space Applications, Antibes, France, September 1993.

J. Balaram and K.V. Prasad, ``Automated Inspection For Remote Telerobotic Operations'', IEEE Conference on Robotics & Automation, Atlanta, Ga. May 1993.

Point of Contact:
J. Balaram,
Mail Stop 198-219
Jet Propulsion Laboratory
4800 Oak Grove Drive
Pasadena, CA 91109
818-354-6770
J.Balaran@jpl.nasa.gov

Perception for Rock Sampling
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In autonomous manipulation research at Carnegie Mellon, a robot first perceives and then grasps objects with a gripper or hand-like tool. The technology has applications in both planetary exploration and excavation on earth. To perform in these natural environments, the perception system must recognize the irregular geometry of rocks and also single out objects for manipulation whether those objects exist in cluttered or barren terrains. To this end, the perception and manipulation system picked up objects successfully in mockup test beds of sand and rock. By functioning autonomously, the perception and manipulation systems avoid the drawbacks of teleoperation-particularly for planetary exploration-where long-distance operation slows communication between the robot and its operators. The robot must view and lift objects with a minimal amount of remote human instruction.
For collecting rocks in planetary exploration, the robot has three main objectives: to sense the terrain and the objects in it, to choose the appropriate three-dimensional model with which to draw a computerized image of the terrain, and to grip the object with either a gripper or hand tool. The robot senses the terrain with a short-range sensor. By projecting light on the scene in a special sequence of patterns, the sensor computes (using triangulation) the positions of all points in the scene.

The perception system then uses three successive perception modules to build an image of the objects in the terrain. The first module, using sensor geometry, conducts a feature detection and shadow analysis of the terrain; this module produces an image of the terrain's shadows and object edges. To fill in the area of the objects, the perception system next chooses between either of two modules: the superquadric surfaces analysis, which represents the object with a three-dimensional mathematical equation, and the deformable surfaces analysis, which defines all points on the object's area. The superquadric surfaces analysis is a better representation of an object that is fairly isolated in the terrain; the deformable surfaces analysis provides a more accurate analysis of objects which are clustered together. Finally, the perception system merges images of the terrain taken from different viewpoints into one composite view. Through merging these images, the robot knows where objects are in relation to itself and to the rest of the terrain.

After choosing between the superquadric and deformable surfaces modules and merging all viewpoints, the perception system must decide which grasping tool to use: the basic gripper, which works best for lifting an isolated rock and for pulling out a rock that is partially buried; or the hand (with fingers) that can negotiate an individual or smaller rock out of a more cluttered area. To determine if the object can be lifted at all, a grasping algorithm matches the dimensions of the tool with the measurements of the rock.

Currently, the perception system relies an remote operators to decide which three-dimensional modules-superquadric or deformable surfaces-or which tool-hand or gripper-to use to grasp the object. Carnegie Mellon researchers are further developing the perception and manipulation system in robots to choose autonomously which perception and grasping methods to use. Researchers will also automate the robot's ability to single out and grasp certain types of rocks.


Point of Contact:
Martial Hebert,
Katsuchi Ikeuchi
Carnegie Mellon University
Field & Mobile Robotics Building
5000 Forbes Avenue
Pittsburgh, PA 15213-3890

STAR: Satellite Test Assistant Robot Infrared Thermal Imaging System
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For the first time spacecraft test engineers at JPL are able to evaluate flight-hardware as it undergoes rigorous thermal/vacuum testing using an advanced imaging system that provides mobile non-contact temperature measurements and high resolution video.

The advanced imaging capability is part of the instrument payload of a newly developed Satellite Test Assistant Robot (STAR). STAR allows engineers to remotely position a multi-axis inspection robot inside the space-simulation chambers during spacecraft testing.

The imaging system consists of three vacuum-rated, high-resolution black and white CCD cameras, one of which is fitted with a remotely operated zoom lens, an advanced infrared thermal imaging radiometer (IR Camera) and a controlled lighting source. The video and thermal images can be viewed, captured and processed remotely at an Operator Control Station (OCS).

The IR Camera is a commercially built unit adapted for use in hard vacuum, low temperature environments. The IR Camera is equipped with a broad-band scanner capable of imaging over the entire 3- 12 um spectrum. It has an electric stirling cycle microcooller which eliminates the need for constant LN2 refilling of the IR detector. It also has an electro-optical zoom capability and high spatial resolution. There are several modes of operation that allow real-time image averaging, line scanning with variable time integration, variable area temperature analysis, histograms, and variable ranges of dual isotherms. All the images are real-time and can be record on standard video tape or captured and stored in a tiff file format. The three B&W CCD video cameras are arranged to provide mono or stereoscopic (3-D) viewing with a scalable field of depth perception.

In September 1993, the imaging system was integrated with a three axis version of STAR and tested in JPL's 10-Foot Thermal/Vacuum Test Chamber. Vacuum levels reached 6 x 10-7 TORR and cold wall temperatures where at -190°C. The imaging system provided vivid images of a Cassini Spacecraft RTG, also under test, which reached temperatures exceeding +250°C.

STAR's advanced imaging system provides a completely new tool-set for evaluating and validating spacecraft and flight hardware prior to launch. Its thermal imaging camera allow engineers for the first time a non-conta ct method for determining temperatures on critical spacecraft surfaces such as so lar panels, radiators and antenna and thermally mapping the entire outside surfaces of a spacecraft. STAR will also aid in the calibration and maintenance of the test chambers. Its most significant contribution may be that it provides engineers with a means of addressing unforeseen anomalies that often occur during the complicated spacecraft testing process.

Point of Contact:
Charles Weisbin
Mail Stop 180-603
Jet Propulsion Laboratory
4800 Oak Grove Drive
Pasadena, CA 91109
818-354-2013
charles_r_weisbin@jpl.nasa.gov

Omniview: Electronic Aim and Zoom Camera
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Microprocessors select, correct, and orient portions of a hemispherical field of view. NASA Langely Research Center, Hampton, Virginia
A video camera pans, tilts, zooms, and provides rotations of images of objects of its field of view, all without moving parts. The camera can be used for surveillance in areas where movement of the camera would be conspicuous or constrained by obstructions. It can also be used for close-up tracking of multiple objects in the field of view or to break an image into sectors for simultaneous viewing, thereby replacing several cameras.

The camera includes a fisheye lens, which creates a circular image of a hemispherical field of view on a charged-couple device (CCD). The image data are stored briefly in an input image buffer for processing. High-speed x- and y-transform digital processors correct the barrel distortion introduced by the fisheye lens and thereby enable the reconstruction of undistorted views of portions of the scene, From a single camera, the system produces as many as four simultaneous different views (virtual cameras) of the scene on a standard RS-170 video monitor at 30 frames per second.

The associated electronic circuitry includes a 32-bit microprocessor with 80-bit floating-point arithmetic support for parametric calculations. It performs control interface functions and calculates the coefficients for the x- and y-transform processors, which are independent arithmetic devices. A human operator programs the microprocessor through a control panel, choosing the magnification, viewing direction, rotation, and offset of the selected portion of the image. Command parameters can also be selected via RS-232 serial communications link.


Point of Contact:
Dr. H. Lee Martin, President
TeleRobotics International, Inc.
7325 Oak Ridge Highway, Suite 104
Knoxville, TN 37931


Machine Vision Guidance for Automated Assembly
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NASA Langley Research Center, Hampton, Virginia
The Automated Structures Assembly Laboratory (ASAL) has successfully assembled and disassembled a 102-member truss structure, including the placement of 12 hexagonal reflector-type panels on the top surface, using a semi-automated system which requires operator attention only when a problem is encountered which the automated system cannot resolve. The system software data base has also been reconfigured so that the system assembles and disassembles a truss beam. The automated assembly system employs commercially available knowledge-based expert systems to plan the assembly, monitor its operation, and assist the operator during error recovery. It also uses a expert system tools plan the sequence of assembly operations and collision-free paths for the robotic manipulator.

For system reliability, a machine vision guidance system has been implemented to locate and capture the truss nodes that the struts are installed into. The machine vision system uses small "lipstick" CCD cameras and uniquely patterned targets located on each node. The targets are fabricated from retro-reflective material and auxiliary lighting is used to bring them into sharp contrast with the cluttered background which includes glare from the truss structure. An image processing algorithm uses pattern matching techniques to identify each node. Since the nodes may be mounted on non-rigid portions of the truss structure, the vision guidance must be capable of locating two nodes and guiding the manipulator arm to capture first one and then the other, and finally pulling both into position for strut installation. The machine vision guidance system proved to be very reliable and robust for the automated assembly operations

Technology areas:


Image processing for machine vision
Guidance algorithms for complex manipulation tasks

Point of Contact:
Ralph Will
Mail Stop 152D
NASA Langley Research Center
1 South Wright Street
Haptom, VA 23681-0001
804-864-6672
ralph.w.will@larc.nasa.gov

Range sensing from wide field-of-view stereo vision

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Robotic vehicles have important applications in planetary exploration, hazardous waste handling, battlefield operations, and factory material transportation. To enable these applications, robotic vehicles must be equipped to automatically detect obstacles in their path. Obstacle detection can be achieved by using range sensors to observe the geometry of the environment, then by analyzing the geometry to find passable routes for the vehicle. However, range sensors have not been available that meet the cost and performance requirements of most applications. JPL has taken a major step forward in this area by demonstrating a practical range sensing system based on stereo vision.
The Wide Field-of-View (WFOV) stereo system, a JPL-based technology developed for the Department of Defense's Unmanned Ground Vehicles (UGV) Project, is a real-time system which produces dense range maps from a stereo pair of cameras mounted on a HMMWV ("Hum-Vee"), the military's modern-day Jeep equivalent. The range data are being used by higher-level vehicle-control systems for autonomously navigating around local obstacles encountered during battlefield maneuvers.

Stereo vision uses two cameras to observe the environment, finds the same object in each image, and measures range to the object by triangulation; that is, by intersecting the lines of sight from each camera to the object. Finding the same object in each image is called matching and is the fundamental computational task underlying stereo vision. Matching objects at each pixel in the image produces a range estimate at each pixel; together, these range estimates form a range image of the scene. Geometric analysis of the range image identifies passable routes. For robotic vehicle applications, the primary alternatives to stereo vision-based range estimation use acoustics, radar, or scanning lasers. Compared to these alternatives, stereo vision has the significant advantage that it achieves high resolution and simultaneous acquisition of the entire range image without energy emission or moving parts. The key issue in making stereo vision practical was to find a combination of algorithms and processors that led to reliable, real-time range estimation with a computer system small and inexpensive enough to use on robotic vehicles.

In a demonstration performed in 1990 for the NASA planetary rover program, JPL used a version of this vision system to show that a robotic vehicle could perform autonomous obstacle avoidance while traversing 100 meters of off-road terrain. This demonstration established the viability and practicality of stereo vision-based range imaging for robotic vehicle applications. The impact of this work is reflected by the adoption of similar approaches for subsequent, NASA-funded robotic expeditions to volcanoes in Antarctica and Alaska, by the potential use of these algorithms in upcoming robotic missions to Mars, and by the transfer of this technology to military robotics programs funded by the Department of Defense.


Point of Contact:
L. Matthies,
Mail Stop 107-102 Jet Propulsion Laboratory
4800 Oak Grove Drive
Pasadena, CA 91109
818-354-3722
matthies@robotics.jpl.nasa.gov

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Unified Approach to Control of Motions of Mobile Robots
Obstacle-detection systems are designed to make the most of limited data-processing resources.
A concept of perception control is guiding the continuing development of obstacle-detection systems for crosscountry navigation of robotic vehicles that are equipped with stereoscopic machine-vision systems. Perception control consists of optimally tuning sensor or processing parameters to increase efficiency of perception under design constraints and design requirements while adjusting to the environment. This particular concept of perception control is oriented toward the need to maximize vehicular safety at a given speed or, conversely, to determine the maximum speed for a given level of safety.

An obstacle-detection system according to this concept uses computing resources efficiently, without resorting to "brute-force" obstacle-detection techniques that often involve more computation than is necessary. Such a system is designed to implement a focus-ofattention approach, in which data are processed from subwindows of the stereoscopic video images of the path ahead, instead of from the entire images, to reduce the computational cost of perception. The image data are processed in the following main steps:


Pyramids (in a symbolic sense) of versions of the stereoscopic images that are band-pass-filtered in a succession of spatial-frequency bands are constructed from the stereoscopic pairs of images.
Cross-correlations are computed on any single level of an image pyramid to estimate stereoscopic disparity at every pixel of the pair of images.
The range (that is, the distance from the video cameras on the robotic vehicle) is computed from the disparity at every pixel
An obstacle-detection algorithm is applied to the resulting range image.
The obstacle-detection algorithm assumes that an obstacle consists of a nearly vertical step displacement on an otherwise nearly flat ground plane. The algorithm does this by using pairs of pixels in the same column of the range image (see figure). If the difference in height between the two pixels in any such pair exceeds a prescribed step height, then an obstacle is deemed to exist at the affected location.
The obstacle-detection system manages the computing resources by adjusting variables at three levels: image resolution, subwindows of attention (steps 2, 3, and 4 can be performed in subwindows), and detection threshold (which can be varied over the scene). The system takes into account its current lookahead requirements and determines the part of the path ahead that must be examined for obstacles at each processing step. The system assumes that the vehicle must stop before colliding with an obstacle, and allowance must be made for image-acquisition and processing time, actuation delay before brakes can be applied, and the braking time to reduce the speed from its current value to zero.

A velocity controller could be designed to operate in conjunction with an obstacle-detection system of this type. The velocity controller could be designed to try to reach the maximum allowable speed, provided that it always checked for the distance to the end of the last processed path segment and applied brakes if necessary (that is, if data on the next path segment did not come in time).


Point of Contact:
Larry Matthies
Mail Stop 107-102
Jet Propulsion Laboratory
4800 Oak Grove Drive
Pasadena, CA 91109
818-354-3722
Larry.H.Matthies@telerobotics.jpl.nasa.gov

Terrain Mapping Using Laser Rangefinders
Perception research in Carnegie Mellon's Planetary Rover program-out of which the walking robot, Ambler, was developed-enables walking robots to create computerized maps of unpredictable, outdoor terrain. Natural terrain is particularly challenging to map, because it does not contain the straight edges and constant lighting of indoor or industrial environments-areas for which perception and mapping technologies have already been developed. To ensure that a planetary rover's perception system will work in unstructured terrain, Carnegie Mellon researchers have tested the rover's perception system over hundreds of different terrains, through changes in lighting level, dust, temperature, and texture of terrain. The perception system was able to navigate the Ambler through these terrains.
Sensing. The rover's perception system views terrain through a laser rangefinder mounted on top of the robot, scanning terrain ahead of the rover within a 60-degree field of view. The rangefinder uses spinning and nodding mirrors to scan the laser beam over the terrain to produce range images. A range image directly measures the distance between the rover and visible points on the terrain ahead. Image preprocessing, which refines the laser image, compensates for "illusions" in lighting and land texture. Darker objects, for example, absorb more laser energy, produce a weaker return signal from the laser, and cause the object to appear farther away. The image preprocessor detects and deletes those pixels that appear unusually far away.

Constructing maps. From the range images, the rover's perception system produces elevation maps. Planetary rovers like Ambler use elevation maps both for locomotion-how and where the rover will place its next step-and for navigation-determining at any moment where the rover is on the landscape, planning a course, and commanding the rover where to go. Elevation maps like the one in Figure 1 allow the rover to plan where and how to place each step without colliding with obstacles in the terrain.

An algorithm in the map-building system shadows regions that are obstructed by intervening terrain. The algorithm also finds and records those regions that lie outside of its field of vision. This way, when the robot's planner asks the perception system for a broader image, the algorithm can report that certain fields of view in the planner's request are not visible. The same algorithm produces elevation maps at whatever resolution the robot's planner asks for.

Compensating for errors. The robot's perception system detects unexpected elements in the terrain produced by the unpredictable effects of lighting, temperature, and texture on the terrain. The robot detects errors by constantly calibrating the difference between its internal terrain map and the height of each foot on the ground. Ultimately trusting what it feels over what it sees, the robot uses a feedback control loop to change its elevation map according to the difference between what it saw and what its legs currently feel from the terrain. By improving the accuracy of the robot's maps, elevation error compensation improves the robot's ability to walk.

Merging maps. The perception system also builds a larger map mosaic out of the smaller map images created from different viewpoints. Generally, the map mosaic algorithm pieces together the smaller maps from the newest to the oldest mapped image. Older map data is used when the navigation planner routes the rover through currently occluded areas that were previously visible. Figure 2 is a map mosaic created from 125 range images acquired at an outdoor site.

While the Ambler walked over kilometers of outdoor terrain over many days of operation, the perception system performed on large amounts of terrain data, successfully producing image after image, map after map.


Point of Contact:
Eric Krotkov
Robotics Institue
Carnegie Mellon University
Pittsburgh, PA 15213 412-268-3058
epk@cs.cmu.edu

3-D Guidance System With Proximity Sensors For Shuttle RMS

A 3-D guidance system which utilizes four proximity sensors on a remotely- controlled mechanical claw has been developed by the Jet Propulsion Laboratory. The sensors feed pitch and range information to a manned control station indicating how the claw is oriented relative to a mating fixture it is about to grasp. The operator then alines the claw so that the fixture is grasped correctly. This system developed for coupling space vehicles can be used in other remote manipulators. The sensors are mounted on the center square frame of the end-effector of a four-claw grapple. Each sensor consisting of an LED source and a photodetector is aimed to sense the object parallel to the shaft (the roll axis) supporting the grapple. Thus four sensitive areas are established ahead of the claws. Using the claws to define four corners of a square, the sensors are mounted at midpoints of the sides of the square. Thus, two orthogonal lines connecting opposite pairs of sensors define the pitch-and-yaw axis of the system. In the simplest arrangement, each sensor is operated in a two-state binary sensing mode, where a zero indicates a too far state while 1 indicates a too close state when the system is in the vicinity of the target. The detection distances of sensors B and D are somewhat shorter than of A and C. Thus a success state when the claws are alined with the target is defined by A signaling 1, B a zero, C a 1, and D a zero. An all-zero state shows that the entire claw is too far from the target and an all-1 that it is too close. A total of 16 combinations is possible (2**4) indicating various misalinements of yaw and pitch axes with respect to the target. One of the 16 states never occurs, when A and C are zero and B and D are 1, because of the detection pattern setup. A more precise alternative would involve three-state sensing. A signal with a value of 2 would indicate too close, 1 on target, and zero too far. Success would be defined by all 1's, and a total of 75 workable logic states would be possible, giving a more accurate feedback.

Point of Contact:
Antal Bejczy
Mail Stop 198-219
Jet Propulsion Laboratory
4800 Oak Grove Drive
Pasadena, CA 91109
818-354-4568
bejczy@telerobotics.jpl.nasa.gov

Self-Motion Manifolds of Redundant Manipulators
A new perspective on redundancy can yield alternative control strategies. NASA's Jet Propulsion Laboratory, Pasadena, California
Self-motion manifolds are introduced in a new approach to the characterization of self-motions of a robotic manipulator that has redundant degrees of freedom. Self motions, which are made possible by the redundancy, are those motions of the robot joints that leave the position of the end effector unchanged. In much of the previous research on redundant manipulators, the approach has been to resolve the redundancy by optimizing the redundant motions of the joints with respect to additional criterion functions while commanding the end effector to follow the desired trajectory. The previous approach has involved the use of a pseudoinverse of the Jacobian matrix (which consists of derivatives of the coordinates of the end effector with respect to the coordinates of the joints) in optimizing locally Q that is, within a small range of redundant motions. In the alternative approach, the kinematics of the robot are reformulated via a manifold mapping that stresses global, rather than local, kinematic analysis. Within this theoretical framework, the infinite number of redundant solutions of the inverse kinematic problem (the problem of finding the trajectories of the joints as functions of the desired trajectory of the end effector) are naturally interpreted as a set of self-motion manifolds (see figure) rather than in terms of the Jacobian null space. This approach is useful in the study of redundant manipulator kinematics. In addition, the problem of the resolution of redundancy can be posed equivalently in this approach as the problem of the control of self-motions, and the self-moti on manifolds are useful in investigating, interpreting, and formulating both local a nd global techniques for the resolution of redundancy. Redundancy can be resolved by direct control of a set of self-motion parameters, by direct control of a rela ted set of kinematic functions defined by the user and the use of these functions to construct an augmented Jacobian, or by optimization with an objective function.

More details can be found in:

Kreutz, K., Long, M. and Seraji, H.: "Kinematic analysis of 7 DOF manipulators," Intern. Journal of Robotics Research, 1992, 11(5), pp. 469-481.

Point of Contact:
Joel Burdick,
Homayoun Seraji,
Mail Stop 198-219
Jet Propulsion Laboratory
4800 Oak Grove Drive
Pasadena, CA 91109
seraji@telerobotics.jpl.nasa.gov


Planning and Reasoning for a Telerobot
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Challenges in transferring technology from a testbed to an operational system are discussed.
NASAUs Jet Propulsion Laboratory, Pasadena, California

A document discusses the state of research and development of the Telerobot Interactive Planning System (TIPS). The system, designed to provide planning and reasoning for telerobots, has been installed in the telerobot testbed at NASA's Jet Propulsion Laboratory. The planning-and-reasoning technology is to be transferred to Gcddard Space Flight Center for use in NASAUs first operational-flight telerobot. When fully developed, telerobots will have both robotic and teleoperator capabilities. The first telerobot, the Flight Telerobotic Servicer, will include a base site on the Space Shuttle or Space Station and a remote site at a workplace, which may be an external structure being built on the Space Station or a satellite that needs servicing. The goal in the development of the TIPS is to enable it to accept such instructions from an operator as the command to replace a given module, then to command a run-time controller to execute operations that will execute the instructions. The runtime controller plans fine motions, grasps, compliant motions, and applications of force and maintains a geometric data base of objects in the workspace. If the run-time controller encounters problems, the TIPS must modify its commands to overcome them. When the TIPS fails, it must allow the operator to take over. The report presents the technical problems and describes the approaches that have been developed to solve them. It describes the prototype TIPS. Finally, it compares the prototype with the architecture it must support in the Flight Telerobotic Servicer.


Point of Contact:
Stephen F. Peters,
Mail Stop 301-250D
4800 Oak Grove Drive
Pasadena, CA 91109
818-354-0157
stevep@parsec.jpl.nasa.gov

David S. Mittman,
Mail Stop 230-200
4800 Oak Grove Drive
Pasadena, CA 91109
818-393-1091
dmittman@beowulf.jpl.nasa.gov

Mark J. Rokey
Mail Stop 301-235
4800 Oak Grove Drive
Pasadena, CA 91109
818-354-1138
mrokey@beowulf.jpl.nasa.gov

Fast motion planners for finding collision-free robot paths
Two fast robot motion planners based on probabilistic motion planning concepts have been implemented. These path planners sacrifice completeness, namely the ability to find a path under all possible circumstances, in return for fast path determination most of the time.
Planner Using Conditional Probability Guided Search

The first path planning algorithm is motivated by the need to reduce the computational burden involved in searching a graph of a deterministic representation of free configuration space available to a robot. This exact approach is practical only if the environment is static, if there are only a few degrees-of-freedom, long motion planning times are acceptable, and if fast computing resources are available.

The fast motion planning approach instead computes the probability of a successful motion of the robot through a region of space. This probability calculation is obtained from a geometric model that captures the effects of objects in the region and the kinematics of the robot arm. The notion of a Motion Transition Probability is defined, which captures the conditional probability of the robot arm moving in a collision free manner in the vicinity of a single obstacle in the environment, while ignoring the effect of all other obstacles. These conditional probablities are then used to guide an on-line search and results, in most cases, in the successful determination of a path within a reasonably short time.

The formal mathematics of this process is reminiscent of a diffusion process, and the algorithm itself can viewed as a Monte-Carlo approach to the solution of an equation for diffusion.

Planner Using Massively Parallel Energy Computations

The second path planning algorithm is motivated by similar needs and is applicable to solve the path planning problem when the robot system has a large number of degrees-of-freedom. It utilizes a massively parallel computational method suitable for implementation on a hypercube type multi-processor computer.

Each obstacle in the environment is described as the intersection of a number of bounding hyperplanes. Each hyperplane of the obstacle contributes to a potential energy term with respect to a number of control points on the robot arm. This energy term is designed such that the peak value is reached when the point on the robot arm is inside the obstacle, and tapers off in a sigmoidal manner as the point recedes from the interior of the hyperplane. A "temperature" parameter determines the rate of this decay, with a higher temperature indicated a slow decay. By taking the product of the energy terms defined for each bounding hyperplane, an overall collision energy term can be defined with respect to the obstacle.

Energy terms are computed for an entire path wherin the energy of the path is defined as the energy of the obstacle/control-point pair at each location along the path. These energy computations lend them to easy parallelization since the energy associated with each obstacle/control-point/path location set can be computed independently from parallel copies of a geometric world-model resident in each of the multi-processor nodes. A parallel gradient computation is then performed to determine repulsive forces along an initial candidate robot path. These forces push the path away from the obstacles towards collision free configurations as they work to minimize the overall path energy. To prevent the iterative process from getting stuck in local energy minima, the initial iterations of the path deformation algorithm are performed using a "high-temperature" model of the energy term. This has the effect of "smearing out" the energy landscape. The temperature parameter is then gradually cooled down as the iterations proceed, by which time local-minima are usually avoided.

The formal mathematics of this process is reminiscent of the simulated annealing method, and the energy term calculations can be thought of as mean-field approximations similar to those used in statistical mechanics.

References:
J. Balaram and H. Stone, ``Automated Assembly in the JPL Telerobot Testbed'', Intelligent Robotic Systems for Space Exploration, Chapter 8, Kluwer Academic Publishers, Norwell, MA, 1992.


Point of Contact:
J. Balaram
Mail Stop 198-219
Jet Propulsion Laboratory
4800 Oak Grove Drive
Pasadena, CA 91109
818-354-6770
j.balaram@telerobotics.jpl.nasa.gov

Neural Network Classifies Teleoperation Data
The neural network identifies phases of tasks.
NASA's Jet Propulsion Laboratory, Pasadena, Califomia

A prototype artificial neural network, implemented in software, identifies phases of telemanipulator tasks in real time by analyzing feedback signals from force sensors on the manipulator hand. This prototype is an early subsystem-level product of a continuing effort to develop an automated system that assists in training and supervising the human control operator: the system would provide symbolic feedback (e.g., warnings of impending collisions or evaluations of performance) to the operator in real time during successive executions of the same task. Such an automated supervisory system could also simplify the transition between the teleoperation and autonomous modes of a telerobotic system. The prototype artificial neural network (see Figure 1) is based partly on the concept of the time-delay neural network, which involves preprocessing of a temporal sequence of input signals through a shift register to turn it into a temporal sequence of spatially arrayed input signals. A basic time-delay neural network contains only feedforward connections and does not exhibit adequate learning accuracy because it lacks an adequate temporal representation of the evolution of a task. To obtain better representation of the evolution of a task, the network is made partially recurrent by adding some connections from output nodes to nodes called Rcontext unitsS that are located in the input layer of neurons. The context units represent the previous state of the neural network, which state, in turn, represents the task phase executed previously. The network was trained by use of a back-propagation algorithm and training data from experimental teleoperation tasks in which a remote manipulator with a hand instrumented to measure forces and torques was controlled by the human operator via a force-reflecting hand controller and remote video monitoring of th e workspace. The tasks included insertion and removal of a peg into and from a hole, insertion and extraction of electrical connectors, and attachment of hook- and-pile pads. The network was then tested by using it to segment a force signal from the peg-in-hole task into task phases. As shown in Figure 2, the network performed the segmentation in real time, albeit with some lags and some errors. On the other hand, the network also exhibited an unexpected ability to recover after misidentifying some phases and to follow tasks, the phase sequences of which differed from those of the training tasks.

More details can be found in: P. Fiorini, A. Giancaspro, S. Losito, and G. Pasquariew, RNeural Networks for the Segmentation of Teleoperation Tasks,S Presence, Vol. 2, No. 1, pp. 1-13, 1993.



Point of Contact:
Paolo Fiorini,
Antonio Gial1caspro,
Sergio Losito,
Guido Fasquariello
Jet Propulsion Laboratory
4800 Oak Grove Drive
Pasadena, CA 91109
charles_r_weisbin@jpl.nasa.gov

Decentralized Adaptive Control for Robots
Precise knowledge of the dynamics would not be required.

NASA's Jet Propulsion Laboratory, Pasadena, California

A proposed scheme for the control of a multi-jointed robotic manipulator calls fo r an independent control subsystem for each joint, consisting of a proportional/integral/derivative feedback controller and a position/velocity/ acceleration feedforward controller, both with adjustable gains. The independent joint controllers would compensate for unpredictable effects (e.g., friction, variations in payload, and imprecise knowledge of the dynamics of the manipulator), gravitation, and dynamic coupling between motions of joints, while forcing the joints to track reference trajectories. The scheme is amenable to parallel processing in a distributed computing system wherein each joint would be controlled by a relatively simple algorithm on a dedicated microprocessor. For the purpose of the scheme, it is convenient to view each joint as a subsystem of the entire manipulator system. The subsystems are considered to be interconnected by disturbance torques that represent the inertial coupling, Coriolis, centrifugal, frictional, and gravitational effects. The problem is to d esign the set of independent joint controllers in which the ith controller generates th e joint torque Ti(t) (where t = time) by responding only to the actual joint-angle trajectory
and the reference joint-angle trajectory
and makes
track
. The adaptive independent controller dedicated to the ith joint would be described by

Ti(t) = fi(t) + [
+
]

as shown in Figure 1, where
is the position-tracking error of joint i. The term
represents an auxiliary signal synthesized by the adaptation scheme to improve the tracking performance and partly compensate for the disturbance torques. The term in the first set of brackets represents the adaptive position/velocity feedb ack controller with the adjustable gains
and
acting on the position and velocity tracking errors
and
respectively. The term in the second set of brackets represents the adaptive position/velocity/acceleration feedforward controller wit h the adjustable gains
,
and
operating on the desired position
, velocity
, and acceleration
, respectively. A theorem derived via the theory of model-reference adaptive control provides the necessary controller-adaptation law in the form of specifications for the auxiliary signal, feedback gains and feedforward gains. The resulting independent-joint-control law can be expressed as that of the combination of the proportional/integral/ derivative feedback controller and the proportional/derivative/second-derivative feedforward controller illustrated in Figure 2. The controller-adaptation laws are simple and involve only a few arithmetic operations. The proportional-plus-integral adaptation laws give a large family of adaptation schemes, from which the most suitable scheme for a particular application can be selected. The use of proportional-plus-integral adaptation law s yields improved convergence and increased flexibility in comparison to the conventional integral adaptation laws.

Point of Contact:
Homayoun Seraji,
Mail Stop 198-219
Jet Propulsion Laboratory
4800 Oak Grove Drive
Pasadena, CA 91109
seraji@telerobotics.jpl.nasa.gov


Robot Arm Dynamic Control by Computer
================================================== ===================
Feedforward and feedback schemes linearize responses to control inputs.
NASA's Jet Propulsion Laboratory,Pasadena, California

A method for the control of a robot arm is based on computed nonlinear feedback and state transformations to linearize the system and decouple the robot end-effector motions along each of the Cartesian axes in the workspace. The nonlinear feedback is augmented with an optimal scheme for the correction of errors in the workspace. The mathematical model of the robot arm is stated in homogeneous coordinates together with the Denavit- Hartenberg four parameter representation of robot-arm kinematics. Using the Lagrangian formulation of mechanics, the dynamic behavior of the robot arm is expressed in matrix/vector form and manipulated to obtain expressions of the types previously found useful in nonlinear-control theory. The resulting dynamic-control mathematical model satisfies the necessary and sufficient conditions for external (or exact) linearization and simultaneous output decoupling. By using non-linear feedback and a diffeomorphic transformation, the non- linear system of dynamical equations is converted into a Brunovsky canonical form and simultaneously output- decoupled. The linearization accomplished here by non-linear feedback is an "external linearization" as opposed to the conventional "internal linearization" (Taylor-series expansion). That is, the nonlinear character of the original system is not changed here by any approximation. Therefore, system linearization by nonlinear feedback can be called "exact linearization" in a control sense. The linearized system is unstable. To stabilize it, a linear feedback loop is added. As long as the feedback matrix is constant and block-diagonal, the system will remain an output-decoupled linear system. A major new feature of the control method is that the optimal error- correction loop directly operates on the task level and not on the joint-servo- control level. The task-level errors are then decomposed by the nonlinear-gain matrix into joint-force or joint-torque-drive commands. The new control method performed well in computer simulations. The augmentation of non-linear feedback with an optimal error-correcting control provides robust performance and assures acceptable tracking errors even when the dynamical parameters of the mathematical model of the robot arm are in error by as much as 30 percent.


Point of Contact:
Antal K. Bejczy
Mail Stop 198-219
Jet Propulsion Laboratory
4800 Oak Grove Drive
Pasadena CA 91109
818-354-4568
bejczy@telerobotics.jpl.nasa.gov

Virtual Reality Calibration Technology and TELEGRIP
JPL recently developed a virtual reality calibration technique that enables reliable and accurate matching of a graphically simulated virtual environment in 3-D geometry and perspective with actual video camera views. This technique enables high-fidelity preview/predictive displays with calibrated graphic overlay on live video for telerobotic servicing applications. Its effectiveness was successfully demonstrated in a recent JPL/NASA-GSFC ORU (Orbital Replacement Unit) changeout remote servicing task. In September 1993, with NASA's recent thrust for industry collaborations, JPL and Deneb Robotics, Inc. established a technology cooperation agreement. In this JPL-Industry cooperative Deneb Commercialization Task, JPL transfers the virtual reality calibration software technology to Deneb, and Deneb inserts this software technology into its commercial product TELEGRIP. This joint technology collaborative work thus enables Deneb to commercialize an upgraded industry product that will greatly benefit both space and terrestrial telerobotics applications.
Key new features of the JPL calibration technique include;


An operator-interactive method is adopted to obtain reliable correspondence data.
A robot arm itself is used as a calibration fixture for camera calibration, eliminating a cumbersome procedure of using external calibration fixtures.
The object localization procedure is added after the camera calibration as a new approach to obtain graphic overlay of both the robot arm and the object(s) on live video and enable effective use of the computer-generated trajectory mode in addition to the teleoperation mode.
A projection-based linear least-squares algorithm is extended to handle multiple camera views for object localization.
Nonlinear least-squares algorithms combined with linear ones are employed for both camera calibration and object localization. Details of the algorithms and their software listings can be found in the recent JPL Document D-11593, which was prepared as part of this JPL-Industry cooperative task.
The JPL virtual reality calibration option is currently being implemented on Deneb's TELEGRIP which is an open architecture based upon Dynamic Shared Objects (DSO's). The TELEGRIP video overlay implementation will be based upon an application programmers interface (API) layer which insulates the overlay developer from the specifics of video hardware, thus enabling support over a wide range of video products. Graphic models can be overlaid in wire-frame or in solid-shaded polygonal rendering, with varying levels of transparency to produce different visual effects.
The virtual reality calibration option implemented on TELEGRIP will provide 1) immediate benefits to NASA for ground-controlled telerobotics servicing in space, 2) immediate benefits to the National DOE (Department of Energy) Labs working on the disposal of nuclear waste, 3) significant enabling technology for the decontamination and decommissioning of commercial nuclear reactors, and 4) foreseeable applications in automotive, medical, and servicing industries.

For more information see:

W. S. Kim, Virtual Reality Calibration: Algorithms and Software Listings with an application to Preview/Predictive Displays for Telerobotic Servicing, Jet Propulsion Laboratory Internal Document D-11593, Feb. 1994.

W. S. Kim and A. K. Bejczy, "Demonstration of a High-Fidelity Predictive/Preview Display Technique for Telerobotic Servicing in Space," IEEE Trans. on Robotics and Automation, vol. 9, no. 5, pp. 698-702, 1993.

W. S. Kim, P. S. Schenker, A. K. Bejczy, S. Leake, and S. Ollendorf, "An Advanced Operator Interface Design with Preview/Predictive Displays for Ground-Controlled Space Telerobotic Servicing," SPIE Conference 2057: Telemanipulator Technology and Space Telerobotics, pp. 96-107, Boston, MA, Sept. 1993.


Point of Contact:
Won Soo Kim,
Mail Stop 198-219
Jet Propulsion Laboratory
4800 Oak Grove Drive
Pasadena CA 91109
818-354-5047
kim@telerobotics.jpl.nasa.gov

Robotic Surgical Assistant for Brain Surgery
================================================== =================
Stereotactic brain surgery is a technique for guiding the tip of a probe or other delicate surgical instrument in the brain, through a small burr hole drilled in the skull and without direct view of the surgical site. Minimizing brain damage as the probe travels from the skull to the surgical target deep in the brain requires a straight-lined trajectory that avoids such vital parts of the brain as the major blood vessels and motor strip. A problem inherent to stereotactic procedures is that the surgeon cannot view the surgical site. Therefore. some 3-D localization of the target area is required. The surgeon must know through what angle and how deep he must insert the probe in order to reach the target. This 3-D localization is usually accomplished by coupling the stereotactic frame with some X-ray device. The Long Beach Memorial Hospital of Long Beach California initiated an experiment to use a robot to provide localization to the surgeon by interfacing a CT image of the patient's brain to the robot's kinematic equations. The procedure consisted of using a stereotactic frame which is affixed to the patient's head on the CT scanner couch. Three N-shaped locators on this frame are used to provide a reference frame to compute the 3-D location of the target image. A robot bolted to the same CT scanner couch is used to provided the coordinates of the target relative to the stereotactic frame. The surgeon, based on observation of CT images, determines the entry point on the skull. The robot is programmed to align a guide, held by the robot's end-effector, with the target and the entry point. The surgeon then inserts instruments through the guide and the entry point, to a depth calculated by the robot.
One main obstacle to using this robot-assisted technique was the inaccuracy of the hospital's commercial robot, a Puma 200. Only large tumors close to the surface of the skull could be treated, requiring a large entry hole. A robot calibration technique developed by NASA's Jet Propulsion laboratory was used to increase the pointing accuracy of the guide held by the robot, enabling application of the procedure to small tumors which were in the interior portion of the brain. This improvement also decreased the necessary skull drilling hole diameter by 50%. Several successful operations were performed using this procedure at the Long Beach Memorial Hospital in the late 1980's.


Point of Contact:
Samad Hayati
Mail Stop 198-219
Jet Propulsion Laboratory
4800 Oak Grove Drive
Pasadena, CA 91109
818-354-8273
Samad.A.Hayati@jpl.nasa.gov




--------------------------------------------------------------------------------
Robot Assisted Micro-Surgery
Building from its established NASA technology base in teleoperations and telerobotics, JPL is developing a new robotic microdexterity platform with important applications to medicine.Through a cooperative NASA-Industry effort, the Robot Assisted Microsurgery (RAMS) task develops a dexterity-enhanced master-slave telemanipulator enabling breakthrough procedures in micro/minimally invasive surgery. A cooperative commercial development agreement with MicroDexterity Systems, Inc. The applicable medical practice includes eye, ear, nose, throat, face, hand, and cranial surgeries. As part of planned task activities, the resulting NASA robot technologies will be benchmarked in actual operating room procedures for vitreous retinal surgery.

The primary objective of this task is to provide an integrated robotic platform for master-slave dual-arm manipulation operational in a one cubic inch work volume at features in the 100 micron range (our goal is to extend these capabilities to features in the 20 micron range). The research is a natural evolution of our extensive experience in force-reflecting teleoperation with disimilar master/slave. Capabilities will include force-reflection and textural tactile feedback, and in situ multiple-imaging modalities for improved surgical visualization and tissue discrimination. Potential NASA applications may include EVA/IVA telescience, bioprocessing, materials process and micro mechanical assembly, small-instrument servicing, and terrestrial environmental testing in vacuum.



Point of Contact:
Paul Schenker
Mail Stop 125-224
Jet Propulsion Laboratory
4800 Oak Grove Drive
Pasadena, CA 91109
818-354-2681
schenker@jpl.nasa.gov

You"ll Find some great and Updated info at the follwing site from which Most of the above Information was compiled,although giving complete details and posting would demand a new thread altogether.

http://ranier.oact.hq.nasa.gov/telerobotics_page/telerobotics.shtm

Excellent posting.

So, where are we trying to go with this?

Although primarily when i started this thread KM,i thought i including everyone else knew everything about Robotics and Robots,but i realised with all the compiled material i didnt know a bit.therefore an exhaustive but collective material is required for the purpose of my own understanding somewhere,i thought this might be beneficial to all of us in understanding of subject thoroghly and its various basics.



bye!

Works for me. A suggestion is that put the compiled version on a personal website and reference it on the sticky topic.

Either way....excellent work.

BTW, I am reading some stuff at http://www.ufoskeptic.org/secret.html, you might find interesting.

Thanks for suggestion..oh yeah! and i"ll check the site out...thanks again.

:)

bye!

The following is an abstract of the seminar attended by my father.
================================================== ==================
Real-Time Statistical Learning for Humanoid Robotics


Stefan Schaal
Computational Learning and Motor Control Laboratory
USC
================================================== =================
Real-time modeling of complex nonlinear dynamic processes has become increasingly important in various areas of robotics and human computer interaction, including the on-line prediction of dynamic processes observed by visual surveillance, user modeling for advanced computer interfaces and game playing, and the learning of value functions, policies, and models for learning control, particularly in the context of high-dimensional movement systems like humans or humanoid robots. To address such problems, we have been developing special statistical learning methods that meet the demands of on-line learning, in particular the need for low computational complexity, rapid learning, and scalability to high-dimensional spaces. In this talk, we introduce a novel algorithm for regression learning that possesses all the necessary properties. The algorithm combines the benefits of nonparametric learning with local linear models with a new Expectation-Maximization algorithm for finding low-dimensional projections in high-dimensional spaces; it can be regarded as a nonlinear and probabilistic version of partial least squares regression. We demonstrate the applicability of our methods in synthetic examples that have thousands of dimensions and in various applications in humanoid robotics, including the on-line learning of a full-body inverse dynamics model, an inverse kinematics model, and skill learning.

In order to speed up skill learning, we also investigated how imitation learning can contribute to teaching humanoid robots. A novel method to encode movement plans in terms of the attractor dynamics of nonlinear dynamical systems is suggested. The shape of the attractor landscapes can be learned, either from a demonstration or by reinforcement learning, using the statistical learning techniques above. Essentially, the suggested methods provide a control theoretically sound tool to acquire a repertoire of movement primitives for various motor tasks, where primitives can rapidly adapt to a dynamic environment. Video presentation will illustrate the outcome of our robot experiments.

Interesting,isnt it?:)

bye!

complex nonlinear dynamic processes: I did this type of work in manufacturing, multi-axis robotics, vision systems and chemical/ refinery safety processes. Now, I am focusing the same process for Business Environment and Economics. I have a long way to go. I have a hard time explaining this to MBA types. But it is fun....

Are you Hans Moravec? (http://www.frc.ri.cmu.edu/~hpm/project.archive/general.articles/1993/Robot93.html)

who will benefit from these developments,....if today the world could be different ,....

but it's not.

One possible future - written in 1993. A century past in internet times. Great stuff. As long as we are not heading towards "Terminator" story, we should be fine. All we need to do is modify our genetics to keep up with the machines.

:D HAHAHAHAHAHAHAHAHAAHAHAHA...(SORRY FOR BIG LAUGH,BUT I AM SURE YOU"LL LIKE THE STORY)
KM,
I FELT TO SHARE THIS STORY OF ISAAC WITH YOU AND OTHERS WHO BY ACCIDENT MAY COME HERE;).
KID BROTHER
================================================== =============
OKAY SO THERE'S A GUY WHO HAS A KID CALLED CHARLIE.HE OUTGROWN,GREAT KID.OVERGROWN A LITTLE BIT HE HAS A PROBLEM COMMUNICATING WITH HIS FELLOW PALS.THIS MAKES HIM A LONER.BUT HIS FATHER INSISTS THAT HE IS A LEADER SO HE IS A LONER AND NOTHING TO WORRY ABOUT.AT LAST JOSIE(CHARLIE'S MOM)TELLS HER HUSBAND TO GET HIM A BROTHER A ROBOTIC BROTHER...HE HESITATES FIRST BUT THEN AGREES.THE "KID" SO CALLED BYE THE FAMILY BECOMES A GREAT PART OF FAMILY.EVEN CHARLIE AND HIS FATHER BEGIN TO LIKE HIM,BUT JOSIE,OH!SHE TREATS HIM LIKE HER REAL SON.SHE ADORES HIM,LOVES HIM TO THE CORE,AFTER ALL KID WAS DESIGNED TO BE A PERFECT BROTHER A PERFECT SON.HE HELPS JOSIE TO GET VEGETABLES,MAKE FOOD,TALK TO HER WHENEVER SHE REQUIRES EMOTIONAL SOOTHING.IN SHORT HE BECOMES A PART OF JOSIE'S LIFE.EVEN CHARLIE LIKES HIM A LOT.KID MAKES HIM FEEL BIG,DOMINATING A LEADER MOST OF ALL,SINCE HE'S PROGRAMMED FOR THAT ONLY.

CHARLIE'S FATHER HAS TO GO FOR ONE DAY SINCE HIS BOSS HAD CALLED,BUT UNFORTUNATELY IN HOUSE FIRE BREAKES OUT,AND WHOLE HOUSE IS WRECKED UP IN THE MEANWHILE FATHER RETURNS TO SEE JOSIE.SHE WAS IN THE LAWN WHEN IT HAPPENED THEY SAID(THE COPS).HE ASKS HIM HOW 'S CHARLIE AND TELLS HER NOT TO WORRY ABOUT KID AS HE CAN BE BOUGHT AGAIN,BUT SHE KEEPS ON MUMURING"I HAD TO MAKE A CHOICE......"FINALLY HE SHOUTS AT HER ONLY TO FIND THAT KID IS LYING ON THE LAWN AND CHARLIE WAS LEFT BY HER IN THE HOUSE...

HE IS NUMBED.
HE SAYS...
YOU SACRIFICED MY SONS LIFE FOR THAT PIECE-FOR THAT-FOR THAT-
FOR THAT PIECE OF TITANIUM-FOR THAT--AAAAAAAAAAAAAAAAHHHHAHAHAAA...



AMAZING AND AMUSING ISNT IT.
PS:THIS WAS TAKEN FROM GOLD,ONE OF HIS LAST COLLECTION OF SCIFI STORIES.

HOPE YOU GUYS ENJOYED IT.I"LL COME UP WITH MORE IF I FIND ANY.
BYE!

Visionary implant

<color=blue><b><i>Atificial Retina</color></b></i>
================================================== ===================
Although it is still early days, the first attempts to make an artificial retina—to restore sight to the blind—look remarkably promising...
---------------------------------------------------------------------
Bypassing a diseased retina to send images direct to the brain

SPECTACLES to aid the blind might seem the stuff of “Star Trek”, but research is in the works that could bring the notion down to earth. A number of groups in America are trying to perfect an “electric retina”, a device that might one day restore vision to millions of people who have lost their sight. To do this, they are calling on the same tricks that were used to create a successful cochlea implant—a device which, in response to sound waves, uses electrical impulses to stimulate nerve cells in the inner ear. These nerve cells fire, sending (slightly imperfect) signals to the brain, where they are interpreted as sound.

Even though researchers are not quite sure how the brain interprets the auditory signals as well as it does, they are now hoping that the same system will work for vision. In theory, an electric retina could function in a similar fashion as a cochlea implant. In healthy eyes, the retina is made up of cells that receive light and translate it into electrical impulses that are sent, via the optic nerve, to the brain. But in diseases such as macular degeneration (the leading cause of blindness in the western world) or retinitis pigmentosa (a hereditary disease causing blindness), the “rod” and “cone” cells that convert light into neural signals gradually degenerate.

Hence the attempts to use a set of tiny electrodes that bypass these cells and send their electrical impulses direct to the ganglion cells—the layer of retinal cells behind the rods and cones. The electrical stimulation should then prompt the ganglion cells to perform the task they are meant for: to transmit information to the optic nerve.

To do this, a tiny video camera, less than three centimetres square, is embedded in the frame of a pair of spectacles so as to capture the scene in front of the wearer. The camera's CCD (charge-coupled device) chip captures and digitises the images, which are then transmitted as radio waves to another, even smaller chip implanted inside the eye. The eye has a ribbon of 100 whispery electrodes attached to its rear. These transmit electrical impulses to the ganglion cells.

That, at least, is the idea. The reality, however, poses a number of engineering challenges. Second Sight of Valencia, California, is trying to harness the best of the academic research to create a device that people can actually use. Its prototypes draw on work done both at the University of Southern California (USC) in Los Angeles and a collaborative group from Harvard University and the Massachusetts Institute of Technology (MIT) in Cambridge, Massachusetts.

One of the biggest problems is finding a way to attach something to the retina's surface. The membrane is no more substantial than a piece of wet tissue paper. And any electrical device left in bodily fluids for long periods tends to corrode. But the hardest task of all is working out how to translate the visual images into electrical impulses that the brain can interpret properly.

As it turns out, the images that the camera records, even when broken down into picture elements (“pixels”), cannot be converted directly. Unfortunately, two electrodes stimulating two sites on a person's retina will not necessarily mean that the patient will see two spots. Deciphering this “neural coding” can be an arduous task. Mark Humayan, an ophthalmologist at USC, has shown that, with a short-term implant, a patient can recognise the letter “E” from five feet away. John Wyatt and Joseph Rizzo, co-directors of the Harvard/MIT retinal implant project, have managed to help a patient see a line of four dots.

Hardly encouraging. But that they have been able to restore, even if only temporarily, a modicum of “sight” to people who had been blind for years is quite an achievement. Clearly, a better understanding will come once the group finds a way to keep an implant in the patient for weeks or even months, rather than just hours.



bye!

From: News and Views | BizNews |
Friday, February 15, 2002

Honda's Humanoid Robot
Rings the NYSE's Bell


By NANCY DILLON
Daily News Business Writer

ove over C3PO, a new humanoid robot from Honda has learned to climb stairs, fetch a cold can of beer, even ring the opening bell at the New York Stock Exchange.

"He's so cute, I want one," said Jeanne Dixon, the wife of a Maryland Honda dealer in town to see the robot unveiled. "I wonder if he can be programmed to take out the garbage?"

No dice, Dixon. The robot can open and close doors on its way to the garbage, use dozens of sensors to spot refuse, even wave at it. But so far it can't lift more than a can of beer.

For Honda, though, it's a two-legged brand-extension.

"Honda has always focused on mobility," said Hiroyuki Yoshino, Honda Motor's CEO. "We didn't see a reason to limit mobility to things we ride or drive."

So far, Honda has built just 20 units, called ASIMO, with one recently rented to a Japanese museum. Costing more than $1 million to produce and carrying a lease price of $150,000 a year, ASIMO is one expensive docent.

Company officials said once ASIMO's target market is determined, the robot will go into mass production and hopefully see its price drop out of the stratosphere.

Named ASIMO after the Japanese words for "leg" and "tomorrow," the remote-controlled robot is 4-feet tall, weighs 115 pounds, and wears a white plastic space suit.

Unfortunately, ASIMO is most likely years away from any Hammacher Schlemmer-style catalogue listing.

Leading ASIMO's first U.S. demonstration at Wall Street's Regent Hotel, Yoshino said Honda is still trying to figure out exactly who would buy the robot and why.

"Perhaps ASIMO will assist the elderly and help with household chores," said Yoshino.

How about wash windows? That's probably another three years out, one engineer said.

And forget about artificial intelligence. This robot's claim to fame is its ability to climb stairs. It won't be saving us from alien invaders anytime soon.

"I'm a bit jealous," said NYSE chairman Dick Grasso shortly after ASIMO became the first robot to ever ring the opening bell. "ASIMO's been able to do what I haven't — get the Dow above 10,000 again."



bye!

The following is an article from Sci-Am.


Long-Distance Robots
The technology of telepresence makes the world even smaller

by Mark Alpert

...........


A week after the World Trade Center disaster, I drove from New York City to Somerville, Mass., to visit the offices of iRobot, one of the country's leading robotics companies. I'd originally planned to fly there, but with the horrific terrorist attacks of September 11 fresh in my mind, I decided it would be prudent to rent a car. As I drove down the Massachusetts Turnpike, gazing at the American flags that hung from nearly every overpass, it seemed quite clear that traveling across the U.S., whether for business or for pleasure, would be more arduous and anxiety-provoking from now on. Coincidentally, this issue was related to the purpose of my trip: I was evaluating a new kind of robot that could allow a travel-weary executive to visit any office in the world without ever leaving his or her own desk.
================================================== ===================
The technology is called telepresence, and it takes advantage of the vast information-carrying capacity of the Internet. A telepresence robot is typically equipped with a video camera, a microphone, and a wireless transmitter that enables it to send signals to an Internet connection. If a user at a remote location logs on to the right Web page, he or she can see what the robot sees and hear what the robot hears. What's more, the user can move the machine from place to place simply by clicking on the mouse. With the help of artificial-intelligence software and various sensors, telepresence robots can roam down hallways without bumping into walls and even climb flights of stairs.

Until now, businesspeople have relied on techniques such as videoconferencing to participate in meetings that they can't attend. Anyone who's seen a videoconference, though, knows how frustrating the experience can be. Unless the participants are sitting right in front of the camera, it's often difficult to understand what they're saying. Researchers are developing new systems that may make videoconferences more realistic [see "Virtually There," by Jaron Lanier; Scientific American, April 2001]. But there's another problem with videoconferencing: the equipment isn't very mobile. In contrast, a telepresence robot can travel nearly anywhere and train its camera on whatever the user wishes to see. The robot would allow you to observe the activity in a company's warehouse, for example, or to inspect deliveries on the loading dock.

The idea for iRobot's machines originated at the Massachusetts Institute of Technology's Artificial Intelligence Laboratory. Rodney Brooks, the lab's director, co-founded the company in 1990 with M.I.T. graduates Colin Angle and Helen Greiner. iRobot's offices are on the second floor of a nondescript strip mall, just above a store selling children's clothing. It's the kind of office that an eight-year-old would adore--machines that look like miniature tanks lurk in every corner, as if awaiting orders to attack. The robots are tested in a large, high-ceilinged room called the High Bay, which is where I encountered a telepresence robot named Cobalt 2. The machine resembles a futuristic wheeled animal with a long neck and a bubblelike head. When the robot raised its head to train its camera on me, it looked kind of cute, like a baby giraffe. Angle, who is iRobot's chief executive, says the company designed the machine to appear friendly and unthreatening. "We wanted to create a device that would be easy for people to interact with," he says. The robot rides on six wheels and has a pair of "flippers" that it can extend forward for climbing stairs. The antenna is fixed to the back of the machine like a short black tail.


TELEPRESENCE ROBOT called the Packbot is designed to do reconnaissance in dangerous environments. iRobot, a company based in Somerville, Mass., has built other mobile machines that can transmit video over the Internet.

After I finished admiring Cobalt 2, I turned to a nearby computer monitor that showed the robot's Web page. In the center of the screen was the video that the robot was transmitting over the Internet. The machine was still staring at me, so I had a nice view of my own backside. The video was grainy and jerky; because the system transmits data at about 300 kilobits per second, the user sees only five or six frames per second (television-quality video shows 30 frames per second). "You're trading off the frame-update rate for the ability to move and control the camera," Angle explains. Transmitting audio over the Internet is more troublesome because of time lags, but users can easily get around this problem by equipping the robot with a cellular phone.

Now I was ready to give Cobalt 2 a road test. Using the mouse, I clicked on the area of the video screen where I wanted the robot to go. The machine's motors whirred loudly as they turned the wheels, first pointing the robot in the right direction and then driving it to the indicated spot. Then I devised a tougher challenge: I directed the machine to smash into the wall on the other side of the room. Fortunately for Cobalt 2, its compact torso is studded with sensors. The machine's acoustic sensor acts like a ship's sonar, detecting obstacles by sending out sound waves and listening to the echoes. Infrared sensors gauge the distance to the obstacles and can also warn the robot if it's heading toward a drop-off. Cobalt 2 stopped just shy of the wall, thwarting my destructive intentions.

The machine that iRobot plans to sell to businesses looks a little different from Cobalt 2. Called the CoWorker, it resembles a small bulldozer--it actually has a shovel for pushing objects out of its path. "It's a robot with a hard hat," Angle says. In addition to a video camera, the machine has a laser pointer and a robotic arm that remote users can manipulate. iRobot has not set a price for the CoWorker yet, but it is already shipping prototype versions to businesses that want to evaluate the technology. The company also plans to introduce a telepresence robot for home use. Such a device could be a lifeline for senior citizens living alone; the robot would allow nurses and relatives to see whether an elderly person is ill or needs immediate help.

Will these mechanical avatars soon be knocking on your door? The fundamental challenge of telepresence is not technological but psychological: I, for one, would have a lot of trouble keeping a straight face if a robot sat next to me at one of our magazine's staff meetings. And can you imagine how most senior citizens would react to the wheeled contraptions? Nevertheless, people may eventually accept the technology if the potential benefits are great enough. For example, an elderly person may decide to tolerate the intrusions of a camera-wielding robot if the only safe alternative is living in a nursing home.

As I wandered through iRobot's offices, I got a glimpse of another telepresence robot called the Packbot. About the size of a small suitcase, this low-slung machine moves on caterpillar treads and, like Cobalt 2, has extendable flippers that allow it to climb over obstacles. The Defense Advanced Research Projects Agency (DARPA)--the U.S. military's research and development arm--is funding the development of the Packbot, which is designed to do reconnaissance and surveillance in environments where it would not be safe for humans to go.

In the aftermath of the September 11 attacks, military officials recognized that telepresence robots could aid the search-and-rescue efforts. So the engineers at iRobot attached video and infrared cameras to the prototype Packbots and rushed them to New York. At the Somerville office I watched an engineer fasten two flashlights and a camera to a Packbot that would soon be taken to the World Trade Center site.

Although the Packbots were too large to burrow into the wreckage, the iRobot engineers used one machine to search a parking garage. Smaller telepresence robots called the MicroTrac and the MicroVGTV--machines made by Inuktun, a Canadian company that sells robots for inspecting pipes and ducts--were able to crawl through the holes in the rubble. The machines found no survivors but located the bodies of several victims.

This grim task was perhaps the best demonstration of the value of telepresence. As I drove back to New York, I felt a grudging respect for the robots--and for the men and women who'd built them.


--------------------------------------------------------------------------------
bye!

Scientists are experimenting with robots that will eventually be able to reproduce, writes Dylan Evans

Thursday February 14, 2002
The Guardian

Last week, a bizarre two-year experiment stirred into life at the Magna Science Centre in Rotherham, south Yorkshire. Before the eyes of guests, professor Noel Sharkey of Sheffield University let loose a fleet of robotic predators and observed as they chased their equally mechanical prey.
Unlike the prey-bots, which can gather energy from high-powered lamps by their solar panels, the predators can only replenish their batteries by sucking the life out of their victims. The artificial vampires do this by plunging sinister metal fangs into the heart of the smaller but more nimble prey and stealing their electricity. What makes this experiment more than just a jazzed-up version of Robot Wars is that Sharkey's machines are genuinely autonomous.

They take their own decisions in real time. Human intervention is reduced still further by allowing the robots' brains to evolve by means of natural selection. Every so often, the virtual genes that encode the best robot control systems are used to create a new generation of predators and prey. The hope is that this process of artificial evolution will lead to the emergence of complex pack behaviour, similar to that which natural evolution has produced in species such as wolves and lions.


The idea of allowing robots to evolve has given rise to a new but rapidly expanding field of research known as evolutionary robotics. Although it shares many of the insights of artificial life, which pioneered the use of genetic algorithms in the 1970s and 1980s, evolutionary robotics is distinguished by its insistence on making the leap from 2D computer-animations to 3D physically embodied machines.


The aim is to remove the human being from the process of robot construction, so that robots can eventually reproduce and maintain themselves without help. In other words, the aim is to create a whole new species from scratch - a species of organism unlike any that has appeared on this planet, composed not of cells and DNA, but of metal and plastic. In the words of Steve Grand, whose groundbreaking computer game Creatures was among the first pieces of entertainment software to incorporate the principles of artificial life, the leading scientists in this field aspire to be nothing less than latter-day Baron Frankensteins.


But what is the point of such an endeavour? Researchers argue that autonomous robots could prove useful in a range of fields, from clearing landmines to space exploration. But concerns have also been raised about the potential dangers. Kevin Warwick, professor of cybernetics at the University of Reading, foresees a time when intelligent robots may pose a threat to the survival of humanity. For the time being, however, such warnings seem premature; the robots professor Warwick put on display during his Royal Institution Christmas lectures in 2000 inspired more laughter than fear, thanks to their reassuring tendency to break down.


Yet it is doubtful that evolutionary robotics will attain its objectives while it concentrates on the aggressive behaviours that have dominated both the research and the public imagination.

As long as scientists focus exclusively on the evolution of robot predators, and TV coverage of robotics merely panders to our appetite for new forms of violence, robots will never get very far. Violent robots may evolve primitive emotions such as anger and fear, but if the history of life on earth is anything to go by, that will only take them to a level of complexity approaching that of insects. To evolve the greater levels of complexity we observe in higher animals such as ourselves, robots will have to acquire a broader repertoire of emotional capacities. It will not be enough for them to get scared and angry; they will need to be able to feel surprise, to experience joy - and even fall in love.


Before you dismiss such talk as far-fetched, consider the following experiment in preparation at the University of Bath. A population of robots will be divided into two sexes. Each robot will try to reproduce by mating with others, but unlike the experiments conducted up to now, these robots will be fussy about who they mate with. The hope is that, by introducing mate choice into the process, artificial evolution will be accelerated, just as occurs with natural evolution. The technical name for this phenomenon is sexual selection, and its fruits are among the most eye-catching in the natural world - the peacock's tail, the bower bird's nest, the baboon's bottom.


Perhaps the most unsettling thing about this development is the unpredictable nature of the outcome. Sexual selection is notoriously capricious, picking on very small features almost at random and taking them to extremes. But even this uncertainty is preferable to the more frightening predictability of the focus on aggressive robots. Who knows? Perhaps robosex will lead to the evolution of robots that care for their human ancestors rather than wishing to destroy them?



bye!

Today, we can make millions of robots that can be used as search and destroy our enemies in caves. With a small fuel cell, a tiny robot can be powered upto 7 days. They can carry plastic explosives and blow themselves upon contact with the enemy in the caves or in a specific designated area.

I think it is a big mistake to give robots the survival and reproduction instincts in the begining. But in about 2030, they may be the ones to manage our finances and food production. We are already using computers to produce goods, pick stocks, predict sales, find cure for cancer and so on....

An interesting reference:

http://www.stephenwolfram.com/publications/talks/97-Cyberfest.html

I dont agree with him in saying that no one is trying seriously.Most of researchers in AI arent serious enough,i dont think so(After all those detailed studies).


bye!

More on AI:

“Computer! Turn on the lights!” Rodney Brooks, director of MIT’s Artificial Intelligence Laboratory—the largest A.I. lab in the world—strides into his ninth-floor office in Cambridge, MA. Despite his demand, the room stays dark. “Computer!” he repeats, sitting down at the conference table.
“I’m…already…listening,” comes a HAL-like voice from the wall. Brooks redirects his request toward a small microphone on the table, this time enunciating more clearly: “Turn on the lights!”

A pleasant tweeting sound signals digital comprehension. The lights click on. Brooks grins, his longish, graying curls bouncing on either side of his face, and admits his entrance was a somewhat rough demonstration of “pervasive computing.” That’s a vision of a post-PC future in which sensors and microprocessors are wired into cars, offices and homes—and carried in shirt pockets—to retrieve information, communicate and do various tasks through speech and gesture interfaces. “My staff laughs at me,” says Brooks, noting he could have simply flicked the light switch, “but I have to live with my technology.”

In the not-too-distant future, a lot more people may be living with technologies that Brooks’s lab is developing. To help make pervasive computing a reality, researchers in his lab and MIT’s Laboratory for Computer Science are developing—in an effort Brooks codirects called Project Oxygen—the requisite embeddable and wearable devices, interfaces and communications protocols. Others are building better vision systems that do things like interpret lip movements to increase the accuracy of speech recognition software.

Brooks’s A.I. Lab is also a tinkerer’s paradise filled with robotic machines ranging from mechanical legs to “humanoids” that use humanlike expressions and gestures as intuitive human-robot interfaces—something Brooks believes will be critical to people accepting robots in their lives. The first generation of relatively mundane versions of these machines is already marching out of the lab. The robotics company Brooks cofounded—Somerville, MA-based iRobot—is one of many companies planning this year to launch new robot products, like autonomous floor cleaners and industrial tools built to take on dirty, dangerous work like inspecting oil wells.

Of course, autonomous oil well inspectors aren’t as thrilling as the robotic servants earlier visionaries predicted we’d own by now. But as Brooks points out, robotics and artificial intelligence have indeed worked their way into everyday life, though in less dramatic ways (see “A.I. Reboots,” TR March 2002). In conversations with TR senior editor David Talbot, Brooks spoke (with occasional interruptions from his omnipresent computer) about what we can expect from robotics, A.I. and the faceless voice from the hidden speaker in his wall.

TR: The military has long been the dominant funder of robotics and A.I. research. How have the September 11 terror attacks influenced these fields?
BROOKS: There was an initial push to get robots out into the field quickly, and this started around 10 a.m. on September 11 when John Blitch [director of robotics technology for the National Institute for Urban Search and Rescue in Santa Barbara, CA] called iRobot, along with other companies, to get robots down to New York City and look for survivors in the rubble. That was just a start of a push to get things into service that were not quite ready—and weren’t necessarily meant for particular jobs. In general, there has been an urgency to getting things from a development stage into a deployed stage much more quickly than was assumed would be necessary before September 11. I think people saw there was a real role for robots to keep people out of harm’s way.

TR: What else besides…

COMPUTER: I’m…already…listening.

BROOKS: Go to sleep. Go to sleep. Go…to…sleep.

COMPUTER: Going…to…sleep.

BROOKS: As long as we don’t say the “C” word now, we’ll be okay.

TR: Did any other robots get called for active duty?

BROOKS: Things that were in late research-and-development stages have been pushed through, like iRobot’s “Packbot” robots. These are robots that a soldier can carry and deploy. They roll on tracks through mud and water and send back video and other sensory information from remote locations without a soldier going into the line of fire. They can go into rubble; they can go where there are booby traps. Packbots were sent for search duty at the World Trade Center site and are moving into large-scale military deployment more quickly than expected. There is more pressure on developing mine-finding robots.

TR: How are you balancing military and commercial robot research?

BROOKS: When I became A.I. Lab director four and a half years ago, the Department of Defense was providing 95 percent of our research funding. I thought that was just too much, from any perspective. Now it’s at about 65 percent, with more corporate funding.

TR: What’s the future of commercial robots?

BROOKS: There has been a great deal of movement toward commercial robots. Last November, Electrolux started selling home-cleaning robots in Sweden. They have a plan to sell them under the Eureka brand in the U.S. There are a bunch of companies that plan to bring out home-cleaning robots later this year, including Dyson in the U.K., Kärcher in Germany and Procter and Gamble in the U.S. Another growing area is remote-presence robots; these are being investigated more closely, for example, to perform remote inspections above ground at oil drilling sites. Many companies are starting to invest in that area. IRobot just completed three years of testing on oil well robots that actually go underground; we’re now starting to manufacture the first batch of these.

TR: How is that different from other industrial robots, like spot welders, that have been around for years?

BROOKS: These robots act entirely autonomously. It’s impossible to communicate via radio with an underground robot, and extreme depths make even a lightweight fiber-optic tether impractical. If they get in trouble they need to reconfigure themselves and get back to the surface. They have a level of autonomy and intelligence not even matched by the Mars rover Sojourner, which could get instructions from Earth. You don’t need a crew of workers with tons of cable or tons of piping for underground inspections and maintenance. You take this robot—which weighs a few hundred pounds—program it with instructions, and it crawls down the well. You have bunches of sensors on there to find out flow rates, pressures, water levels, all sorts of things that tell you the health of the well and what to do to increase oil production. They will eventually open and close sleeves that let fluids feed into the main well pipe and make adjustments. But the first versions we’re selling this year will just do data collection.

TR: The computer that turned on the lights is part of MIT’s Project Oxygen, which aims to enable a world of pervasive computing. As codirector, what are your objectives?

BROOKS: With Project Oxygen, we’re mostly concentrating on getting pervasive computing working in an office environment. But the different companies investing in Project Oxygen obviously have different takes on it. Philips is much more interested in technologies to make information services more available within the home. Delta Electronics is interested in the future of large-screen displays—things that can be done if you have wall-sized displays you can sell to homeowners. Nokia is interested in selling information services. They call a cell phone a “terminal.” They want to deliver stuff to this terminal and find ways we can interact with this terminal. Already, Nokia has a service in Finland where you point the cell phone at a soda machine and it bills you for the soda. In Japan, 30 million people already browse the Web on their cell phones through NTT’s i-mode. All these technologies are providing services from computing in everyday environments. We are trying to identify the next things, to see how we can improve upon or go beyond what these companies are doing.

TR: To that end, Project Oxygen is developing a handheld device called an “H21” and an embedded-sensor suite called an “E21.” But what, exactly, will we do with these tools—besides turn on the lights?

BROOKS: The idea is that we should have all our information services always available, no matter what we are doing, and as unobtrusive as possible. If I pick up your cell phone today and make a call, it charges you, not me. With our prototype H21s, when you pick one up and use it, it recognizes your face and customizes itself to you—it knows your schedule and where you want to be. You can talk to it, ask it for directions or make calls from it. It provides you access to the Web under voice or stylus command. And it can answer your questions rather than just giving you Web pages that you have to crawl through.
The E21s provide the same sorts of services in a pervasive environment. The walls become screens, and the system handles multiple people by tracking them and responding to each person individually. We are experimenting with new sorts of user interfaces much like current whiteboards, except with software systems understanding what you are saying to other people, what you are sketching or writing, and connecting you with, for instance, a mechanical-design system as you work. Instead of you being drawn solitarily into the computer’s virtual desktop as you work, it supports you as you work with other people in a more natural way.

TR: How common will pervasive computing become in the next five years to 10 years?

BROOKS: First we have to overcome a major challenge—making these devices work anywhere. As you move around, your wireless environment changes drastically. There are campuswide networks, and cell phones in different places with different protocols. You want those protocols to change seamlessly. You want to have these handheld devices work independent of the service providers. Hari Balakrishnan [an assistant professor at MIT’s Laboratory for Computer Science] and students have demonstrated the capability—which has had great interest from the corporate partners—in having a totally roaming Internet, which we don’t have right now. That’s something I expect will be out there commercially in five years.

TR: And in 10 years?

BROOKS: In 10 years, we’ll see better vision systems in handheld units and in the wall units. This will be coupled with much better speech interfaces. In 10 years the commercial systems will be using computer vision to look at your face as you’re talking to improve recognition of what you are saying. In a few years, the cameras, the microphone arrays will be in the ceiling in your office and will be tracking people and discriminating who is speaking when, so that the office can understand who wants to do what and provide them with the appropriate information. We’re already demonstrating that in our Intelligent Room here in the A.I. Lab. I’ll be talking to you—then I’ll point, and up on the wall comes a Web page that relates to what I’m saying. It’s like Star Trek, in that the computer will always be available.

TR: What is the state of A.I. research?

BROOKS: There’s this stupid myth out there that A.I. has failed, but A.I. is everywhere around you every second of the day. People just don’t notice it. You’ve got A.I. systems in cars, tuning the parameters of the fuel injection systems. When you land in an airplane, your gate gets chosen by an A.I. scheduling system. Every time you use a piece of Microsoft software, you’ve got an A.I. system trying to figure out what you’re doing, like writing a letter, and it does a pretty damned good job. Every time you see a movie with computer-generated characters, they’re all little A.I. characters behaving as a group. Every time you play a video game, you’re playing against an A.I. system.

TR: But a robotic lawn mower still can’t be relied upon to cut the grass as well as a person. What are the major problems that still need solving?

BROOKS: Perception is still difficult. Indoors, cleaning robots can estimate where they are and which part of the floor they’re cleaning, but they still can’t do it as well as a person can do. Outdoors, where the ground isn’t flat and landmarks aren’t reliable, they can’t do it. Vision systems have gotten very good at detecting motion, tracking things and even picking out faces from other objects. But there’s no artificial-vision system that can say, “Oh, that’s a cell phone, that’s a small clock and that’s a piece of sushi.” We still don’t have general “object recognition.” Not only don’t we have it solved—I don’t think anyone has a clue. I don’t think you can even get funding to work on that, because it is just so far off. It’s waiting for an Einstein—or three—to come along with a different way of thinking about the problem. But meantime, there are a lot of robots that can do without it. The trick is finding places where robots can be useful, like oil wells, without being able to do visual object recognition.

TR: Your new book Flesh and Machines: How Robots Will Change Us argues that the distinctions between man and machine will be irrelevant some day. What does that mean?

BROOKS: Technologies are being developed that interface our nervous systems directly to silicon. For example, tens of thousands of people have cochlear implants where electrical signals stimulate neurons so they can hear again. Researchers at the A.I. Lab are experimenting with direct interfacing to nervous systems to build better prosthetic legs and bypass diseased parts of the brain. Over the next 30 years or so we are going to put more and more robotic technology into our bodies. We’ll start to merge with the silicon and steel of our robots. We’ll also start to build robots using biological materials. The material of us and the material of our robots will converge to be one and the same, and the sacred boundaries of our bodies will be breached. This is the crux of my argument.

TR: What are some of the wilder long-term ideas your lab is working on or that you’ve been thinking about?

BROOKS: Really long term—really way out—we’d like to hijack biology to build machines. We’ve got a project here where Tom Knight [senior research scientist at the A.I. Lab] and his students have engineered E. coli bacteria to do very simple computations and produce different proteins as a result. I think the really interesting stuff is a lot further down the line, where we’d have digital control over what is going on inside cells, so that they, as a group, can do different things. To give a theoretical example: 30 years from now, instead of growing a tree, cutting it down and building a table, we’d just grow a table. We’d change our industrial infrastructure so we can grow things instead of building them. We’re a long way away from this. But it would be almost like a free lunch. You feed them sugar and get them to do something useful!

TR: Project Oxygen. Robots. Growing tables. What’s the common intellectual theme for you?\

BROOKS: It all started when I was 10 years old and built my first computer, in the early 1960s. I would switch it on and the lights flashed and it did stuff. That’s the common thread—the excitement of building something new that is able to do something that normally requires a creature, an intelligence of some level.

TR: That excitement is still there?

BROOKS: Oh yeah.

Link: http://www.techreview.com/articles/qa0402.asp

Consider this:

BROOKS: Technologies are being developed that interface our nervous systems directly to silicon. For example, tens of thousands of people have cochlear implants where electrical signals stimulate neurons so they can hear again. Researchers at the A.I. Lab are experimenting with direct interfacing to nervous systems to build better prosthetic legs and bypass diseased parts of the brain. Over the next 30 years or so we are going to put more and more robotic technology into our bodies. We’ll start to merge with the silicon and steel of our robots. We’ll also start to build robots using biological materials. The material of us and the material of our robots will converge to be one and the same, and the sacred boundaries of our bodies will be breached. This is the crux of my argument.

I am so excited. AI is here, AI is here. I guess I did not know that cochlear implants are AIs in disguise. The pace makers too? Something new you learn everyday....

Interesting KM,
Recently while in Singapore i went to a Discovery Channel convention,there were people from Hong Kong who were in charge of Robotics division and were monitering Aibo Robot.they always refferred AI programming as Neural net programming,(which i know most of us disregard as an AI approach).they were giving lectures on how to make Robots with Neural nets so as to enable them with Survival instincts,as a learning mechanism.the first trial of such a proceedure would be essentiallya reptillian class Robotics as Hans says.

Okay,here's another one.
there's a Robot festival in Hong Kong,are you going KM?:)
this will include lectures etc.

bye!

Robots--working for Japan's future?

Reuters
April 2, 2002, 11:10 AM PT

By the end of the decade, the people who disarm bombs and search for survivors after a disaster may no longer need to put their lives on the line. A machine, possibly made in Japan, may be able to handle the dangerous stuff.
That is one goal of the Japanese government's $37.7 million Humanoid Robotics Project (HRP), which aims to market within a few years robots that can operate power shovels, assist construction workers and care for the elderly.

In the process, a new multibillion-dollar Japanese industry could be born.



"Just as automobiles were the biggest product of the 20th century, people might eventually look back and say that robots were the big product of the 21st century," said Hirohisa Hirukawa, a researcher for the government-affiliated National Institute of Advanced Industrial Science and Technology.

Hirukawa heads a group that helped to develop HRP-2, a silver and blue humanoid robot that stands 5 feet tall, weighs 128 pounds and looks a bit like a child wearing a spacesuit.

The robot, co-developed with Kawada Industries, Yaskawa Electric and Shimizu, is the latest in a series of humanoid robots unveiled by Japanese researchers in the last few years.

The government hopes their efforts will eventually enable robots to walk out of the factory--virtually their only domain at present--and into homes, offices, hospitals and any other place where humans toil.

It also wants to capitalize on the technological edge of Japan, the global leader in robot production and home to more than half of the world's industrial robots.

"We want to create a new market exploiting the technology Japan has accumulated, and to help strengthen the economy over the medium to long term," said Kenichiro Yoshida, deputy director of the Trade Ministry's industrial machinery division.

The Japan Robot Association, an industry body, estimates that the robot industry could grow to $22.61 billion by 2010. The figure has hovered around $3.8 billion for the past few years.

The group predicts the expansion will be led by robots that perform everyday tasks and believes that, while there are no such robots on the market now, they could be ringing up annual sales of $11.3 billion by 2010.

"We want robots to be able to function around humans and be useful in areas other than entertainment," Yoshida said.

For the industry to take off, however, technology must become far more advanced and, perhaps more critically, researchers will need to find useful roles that humanoid robots can play in society.

The HRP-2 appeared before the public for the first time at Robodex 2002, a four-day exhibition at the end of March that featured various robots developed by Japanese corporations and universities.

Visitors watched the blue-helmeted android help a human carry furniture about, and an older prototype drove a forklift.

"There is demand for robots that can be used in dangerous places and disaster areas," Hirukawa said, noting that workers could, for example, operate construction machinery from a safe distance via a remote-controlled HRP-2.

He hopes that perhaps 10 of the HRP-2 robots could be sold within five years of the state-run project ending in March 2003.

"Once we can sell 1,000 robots, I think the state's role will end and we will enter a natural mass-production spiral," Hirukawa said. "But I can't see yet how that will happen."

And Hirukawa says it will be a long time before humanoid robot technology is advanced enough to foster a major industry.

"I think the earliest we will see robots doing household chores will be by 2025, or 2050 at the latest," he said.

The Trade Ministry, however, wants to find a quicker way to build up a new robot industry and is beginning to examine options other than humanoid machines.

The trade ministry's Yoshida said a new project aimed at developing household robots would not focus on humanoid robots, and the ministry was considering whether to continue the humanoid robot project after March 2003.

"Robots don't have to be humanoid to be useful in homes," he said. "We want to make robots as quickly as possible that can be used in homes or for disaster areas."


Interesting,isnt it?

bye!

It is the same Japanese who declared in early 80's to built an AI using 5th generation super computers in 10 years. Neural net will work for specific activities - we shall see how it goes.

People have a tendency to over simplify issues and solutions. To give you an example, here in US, certain government work needed so many people like yesterday to redesign certain computer systems. Our company has been trying to supply those hard to find people for the last two months - there are so many road blocks, you would not believe. It is not as simple as it sounds...

BTW, no. I will not be going to Hong Kong. I have so much stuff in my head, I need to unload it first on some fun projects. Then I can go - to refill my idea tank.

I think you"re right in terms of over-hyping being done for AI.The development although promised to follow Moore's law has been not even at snail's pace,since we were to enter 21st century with HAL type of walking machines.the days with android type of Robots are long way but developments are underswing and must continue with positive flexible approaches(that is maturing,self evolving and utilizing of new ideas),i am sure we will reach our stereotype scifi HALs,CALs etc...who will evolve with us to a new astonishing and a great future.

Oh yeah!i almost forgot...you must check these interesting links...

http://www.fzi.de/ids/WMC/walking_machines_katalog/walking_machines_katalog.html

http://robots.net/robomenu/


bye!

Berry berry interesting....:D

I think Space craft healing concpet can be used in Robots as well...

check out self healing space craft thread in Astronomy forums...:cool:



bye!

My 2 cents worth. Re the bits and pieces to make a robot and I am talking software and hardware. We have enough hardware to make an intelligent robot - even though we are not sure how much hardware that is!

In the software though we have at least one big step to go. We can program logical thinking, planning , imagination, emotions. What is missing is patterns. If we could program to recognise patterns we could build memory like a humans, see a cup in a picture, hear a word in the noise from the microphone.

Yes; neural nets, and a brute force approach ( see chess programs and current voice recog programs ) are a start at pattern recognition, but each has problems.

There is a little math and logic that strongly sugests the problem of seeing a cup, hearing a word, putting a spatial map into the "thinking" are all the same problem. Solve one and you solve them all. What we dont know is if this will be tomorrow or next century.

When we have a solution, researchers will put theat and the stuff we have together and this will likely be an eye-opener. But we are not sure what else we may run into and how easy the problems will be to solve after this is tried.

One of the sayings AI people have

"The easier it is for a human to do the harder it is for a Computer/Robot and vice versa. "

You can see a door without thinking a computer really struggles with this, A human struggles to add up a million numbers, but a computer can add up a million numbers easily and get it right. (Unless of course it works for a bank, tax department or Enron... :-)

Ahh...patterns...we can learn a lot from how humans process patterns. Here is the abstract of a paper that discusses such item:

Talk about 'timing' (Synchronicity?) - I just read the abstract and was thinking about whether I should post it in sciforums. The opportunity presented itself when I saw the fresh posting on this topic....enjoy.


Three-dimensional orientation tuning in macaque area V4


David A. Hinkle & Charles E. Connor

Department of Neuroscience, Johns Hopkins University School of Medicine and Zanvyl Krieger Mind/Brain Institute, 338 Krieger Hall, 3400 North Charles Street, Johns Hopkins University, Baltimore, Maryland 21218, USA


Tuning for the orientation of elongated, linear image elements (edges, bars, gratings), first discovered by Hubel and Wiesel, is considered a key feature of visual processing in the brain. It has been studied extensively in two dimensions (2D) using frontoparallel stimuli, but in real life most lines, edges and contours are slanted with respect to the viewer. Here we report that neurons in macaque area V4, an intermediate stage in the ventral (object-related) pathway of visual cortex, were tuned for 3D orientation—that is, for specific slants as well as for 2D orientation. The tuning for 3D orientation was consistent across depth position (binocular disparity) and position within the 2D classical receptive field. The existence of 3D orientation signals in the ventral pathway suggests that the brain may use such information to interpret 3D shape.

A very interesting thread in an area that I'm just beginning to dig into, but I haven't been able to wade thru everything that zion has posted (there is a LOT there!). One area that I think you've been missing in this discussion of robots and where they will lead is nanotechnology. Consider the effect of "Utility Fog (http://nanotech-now.com/utility-fog.htm)" on the direction that robotics might go in the future.

Also, although you paint a "rosy" picture of where things might go in the future, have you speculated on the "dark" side of the technology?

dark side of the technology

Hmmm....let me think...like using an airplane as a weapon of mass destruction? Honestly, There are so many ways to use technology in a bad way that I do not want to discuss it specifically in the area where someone might use it for evil purposes. On the otherhand there are some areas we can discuss that has a long term negetive effect.

For example - sometime soon, we can map and understand every pair of gene in a plant. We can design plants to fight disease, be intelligent so that they can adapt to harsh environment and produce apples and pears in the same tree. The long term ramification can be devastating, if those intelligent plants after 50 years turn against humans thinking we are also pests.

I do keep track of nanotechnology. nt by itself is an architecture and not a product. The products are computers, display units, robots, etc. physical items that perform one or more tasks. So, if we can focus on the tasks or objectives and work backwards as to the architecture that can provide that objective - then whether we use nanotechnology, material science, quantum structures etc...we can get there.

Anyway...yes zion posted tons of stuff. Once you go through them, you can become an expert....this is an open call to high end information technology architects to add here so that we can produce our own idea that can rival IBM Lab.

Originally posted by kmguru
For example - sometime soon, we can map and understand every pair
of gene in a plant. We can design plants to fight disease, be intelligent so that they can adapt to harsh environment and produce apples and pears in the same tree. The long term ramification can be devastating, if those intelligent plants after 50 years turn against humans thinking we are also pests.

Bah. Too complicated. I was thinking more simply. In the case of robotics, in the long run, we may develop a utopian society where robots perform most every task (as some of what zion posted alluded to), but, in the short run, there is the issue of keeping an ever growing human population productively employed until we sort out the structure of the utopian society (these things don't happen over night). Mix in nanotechnology and now you have a population of robots that can grow as fast as (or faster than) the human population. If they learn to "evolve" along the way... :bugeye:

Have you read Bill Joy (http://www.wired.com/wired/archive/8.04/joy.html)? There are many more scenarios where this could go awry. Remember what's happening today with the Internet and the growing legion of crackers that are finding ways to subvert the technology. The good guys need to be "right" all the time while the bad guys need to be "right" only once (a la 9/11).

Yes I read Bill Joy and I like his thoughts. There is no such thing as good without evil. I can be as evil as good. It is all in the balance knowing the long term cause-effect. It is that ying-yang thing. If one can look at the big picture and understand the cause and effect then the unpleasant side-effect can be minimized.

I also look forward to the robotic and automated world where the food and shelter comes free to every newborn. In the Maslov's heirarchy of needs, I would like to see that the human needs are eleveted to self-actualization.

In India, two thousand years ago, a structure was in place where upto first 16 years, a person was pampered and nurtured and educated. Then that person enters the society to do the work to feed and raise his family and society. At something like 60 years of age, the person usually leaves the society and travels seeking self-actualization. His food and shelter is met by churches and donation type institutes (like Salvation Army) along the way. Some wrote memoir and still others stories and their experience enriching the society. It has been done and proven very successful. History can be repeated again....this time using technology to overcome the population issue.

There are many who view the future as a very nice place and, for the most part, I think I agree with them. The problem will be getting from here in the present to that future. There is going to be a LOT of shakeup before utopia arrives in the next century:


Advanced robotics will lead to massive unemployment as menial jobs are taken over by the robots.
This will lead to the rich getting much richer and the poor getting much poorer.
Which may lead to partial collapses of various economies as people struggle to find money for needs let alone wants.
Nanotech will enhance this effect if it happens at the same time as it will make it easier to assemble most anything thus idling the other producers of goods.


Ultimately, I find the question to be, if these two technologies begin delivering on their promise, will the world know how to convert to times of "economic" plenty? Most people would jump on this question and say "of course" (as they jumped on Bill Joy), but think about it against all that you see going on in the world today. Many great things will undoubtedly happen, but will they all be good and, if not, how bad could they be?

As you know, we all comment, debate based on what we read as well as based on hands on experience. I usually would put a little more emphasis on hands on than information gathered from books etc. For example, before I let a doctor do brain surgery, I would like to know how many he has done in the past and how successful...right?

So, IMHO, my comments on robotics and automation is based on my hands on experience as an architect, designer and troubleshooter for some of the best automation systems in the world since 1970 - in chemical, refinery, manufacturing, mining etc. Every type of products you buy today is the result of partial automation (you still need some people to monitor and manage). In early seventies, when US was going through automation, productivity rose significantly. However the union people got scared that there will be massive unemployment. In 1983, I did a comparision and US automation was such that, you needed 12 times more people in China and 10 times more people in India to produce the same goods. Since then other countries started automating like crazy - so far there has not been a major non-recoverable unemployment situation anywhere due to automation. Same thing can not be said for policy changes by stupid politicians - but that is different...

So...we will find a balance ....