This sounds really cool. I'm guessing that they are going to apply the learning algorithm to some of the biped robots they have, like Elvina and maybe Priscilla when that one is done. Instead of pattern bipeds like Asimo and SDR-4X, perhaps there will be one that goes through a learning pattern of gait generation. I'd seen some simulations of biped learned walking somewhere, but can't remember where.

I guess some of you suspected I would cough on this one :) Steve, Did you post this just to bend my encoders?

Let's see, build hardware which is designed to succeed at some task, write a program to try various pre-picked, pre-written subroutines using the random number generator built into the compilier which was written and tested by a team of programmers, add an if-then statement to some sensor that was designed to determine if the machine accomplished the pre-selected goal which was concieved by the designer, keep track of the options tried and their success/failure score. When one works, write down the time it took and proclaim victory! Oh, forgot to mention that the creator of the project had to also provide power during this experiment.

Many of us hobbyist did projects like this 23 years ago with Erector sets and 8080-based assembly language programs. But we were naive and didn't have axe grinding equipment or a slashdoting to to put on our resume.

Evolution? No. I thought evolution required that order and intelligence come from nothing? So go ahead and impress me, go to a local junkyard and collect random parts, you can stare but please don't touch. When it learns to fly, then let me know. Ok, not fly, I'll accept a small fart, the wispy kind that don't smell much :)

If this is evolution, it works pretty well with an intelligent designer behind the wheel ;)

Ahhh, the sound of flame throwers being loaded throughout the land.....

Peace, Roger

Heh... actually it didn't occur to me at the time that anybody would have a problem with it. With respect to the evolution vs. creation thing, it's been discussed to death a million other places, so I don't want to rehash that here.

But with respect to the experiment itself, I don't think you could successfully argue that the flying algorithm which evolved in this case was pre-picked or pre-written. Genetic agorithms and other types of evolutionary software work in the same way evolution in the real world does (or, is believed by some to work, if you prefer). The source code for this experiment may be available for examination in which case you could prove conclusively whether or not it was rigged. If it's not available, you can find other genetic and evolutionary algorithm packages on the net that are open source, fully documented, and still produce real results. There's an old proverb: If it happens, it must be possible. ;-)

I thought it was really silly how they claimed evolution took zillions of years, but their experiment did the same thing in three hours. Give me a freaking break. These scientists got the output they expected. This was not really emergent behavior from nothingness, it was epected results from choosing the best pattern. If they really wanted to reproduce evolutionary behavior (which I don't believe they can), they would have to just create a innumerable bunch of neurons and see what behavior happened. For that matter, they would most likely have to make zillions of these creatures hoping with a vast inprobability that within their lifetime one would perchance type the works of shakespeare much less figure out how to fly. This experiment was very contrived and their statement bogus. Instead what they did was they gave critter instructions on what to do, what goals to attain, and golly gee wiz in three hours it did it. That's hardly reproducing evolutionary behavior that they claimed they did in three hours that the wourld couldn't do for zillions of years. Like comparing apples and oranges I'd say.

Perhaps seperating out the hype of the author of the article from the work the researchers had done might be more productive. The use of the descriptions of "evolution" and the story presentation to produce something readable and understandable to lay folks, while furthering a supposition of the evolutionary model is the "journalists" take. But if you look at the possibilities that the researchers Wolff and Nordin are working on, disregarding their choice of terminology, it could be a pretty good experiment. Perhaps others have done this research before in other forms amd with other hardware, but have they bothered to write about it, where others could learn of their results without having to reinvent the wheel? I know, part of the fun of building robots is learning how different objects work together.

I read an article a year or so ago in EE Times about a researcher who wrote a program to randomly program an FPGA, with the intent of evolving an oscillator.

As I remember it, after several (thousand?) iterations, it did indeed produce an oscillator. However, on inspection of the FPGA's cells, it was an oscillator of bizarre construction, and a different FPGA wouldn't necessarily oscillate from the same firmware, since the evolved circuit relied on parameters that would vary between FPGAs.

I think Attila and Ghengis used a similar method to learn to walk (this was discussed in an issue of Scientific American magazine).

We did a story on the FPGA back in 2001. The link to the news article has long since gone dead but the link to the Adrian Thompson's home page is still good and he has quite a few papers on the subject of artificial evolution of electronic circuits and robotics. Also there's a very brief summary of the FPGA project here. The cool thing about the FPGA project is that it evolved a circuit layout that was extra weird - it not only worked but initially no could figure out why it worked. There were groups of cells in the FPGA that were completely unconnected to the main circuit but if they were removed the system stopped working. If I remember correctly, it was eventually determined that the evolved design was using the RF interference created by the operation of the isolated group of cells to influence the timing of the main circuit. The main circuit included a group of cells that were connected in a way which made them very sensitive to RF interference. The whole RF interaction was the part that probably wouldn't work if you moved the design to a different FPGA chip.

There are many similar stories of evolutionary techniques in software design, where the resulting software succeeds at accomplishing a task, but the researchers are initially unsure how because the methods being used are unusual or new. There is the now famous example of Thomas Ray's artificial life experiment. The genetic algorithms he was using to create the "genome" of his software-based lifeforms stumbled onto the code optimization technique called "unrolling the loop" which, while well-known in some computer science circles was unknown to Ray at the time. One of the interesting things about most genetic algorithm projects is how often the evolved designs are completely unexpected by the researchers doing the projects.

I think one of the things that confuses people with genetic algorithms (or evolution in general) is the use of randomness as an input to a fitness function. Some people seem to have an emotional reaction to "randomness" and never see the part about "fitness" (I guess this is where the analogies to tornadoes in junkyards come from?). I saw a great genetic algorithm tutorial once that ran two simultaneous examples of evolution - one with random input and a fitness function to pick survivors and one with just random input. The goal was to spell out a particular sentence. The random input was a string of randomly selected characters. The simulation with the fitness function reached the goal in a fairly small number of iterations (hundreds or thousands) whereas the one that just relied on randomness never did even after running for millions or billions of iterations - because the odds against it were so astronomical.