I have been thinking for a long time about how to harness the power of the internet into programming robots. I can imagine a robot here which I give some form of task. Let's say (and this would be relevant) finding a wall socket and plugging itself in. The task of successfully identifying a wall socket, moving the robot to a correct location, and inserting some sort of plug would seem monumental. There is a metric shit tonne (thanks CTC!) of environmental problems or things which could go wrong. Rather trying to think of all the possibilities, we would do better to "grow" the program out of the environment. Let's start with the first step "Identify a Wall Socket". What sensors can we use to find a wall socket? It might be possible to use a gaussmeter or hall effect meter. A map of the room might help too. But the way I am initially going to try to locate the wall socket is the same way people do, by using vision.
Haar Training starts by collecting a large number of "positive" and "negative" images. Positive images are images in which the object exists. Negative images are simply images which the object does not exist. During part of the training the objects in the positive images need to be manually selected by a bounding box. This is one of the most labor intensive parts of training. When I read this, I instantly thought of a fantastic project which involved Michael Jackson's white glove. What if the concept was used to create a library of Haar Classifiers? A site dedicated to train our new robot overlords !
The White Glove Tracking Project
In addition I have thought what if a robot was able to distinguish an object from its surroundings. What if it had the capability to query in an attempt to identify that object?
For example Imagine the following scenario:
For anyone who has had a child this is what happens for a couple of years. The toddler runs around and asks anyone near by "This?" and points to some person, place or thing. Now imagine that same concept used to train a robot, but distributed over the internet so there is not just one or two parents, but thousands or hundreds of thousands. How quick / accurate would the training be? How useful would the data and libraries be? I don't know... but it would be interesting to try this experiment, No?
So, you may think. Damn! that's a huge amount of effort just to get my servo to move from A to B. Yeah, it would be - but a one-dimensional Servo positioning problem is not "hard". The thing I'm thinking about is "walking". Human locomotion involves at least 8 joints, hundreds of muscles, inner ear, the sense of touch, and the "where is my foot?" sense. I forgot what this is called, but the name is buried in a RadioLab episode.
Imagine sitting down and trying to figure out all of those commands - Like "I am going to move my knee 3 degrees now", then "I am going to flex my butt slightly more" ... blah blah blah.
Nope, no one does that (although there apparently is a disease you can get where you have to do this)...
What happens, is when your a little shmoo, you start kicking and crawling, and grabbing at stuff.... then you balance yourself ... then BAM ... you fall on the coffee table. FAIL!
Try again... shaky legs, wobble, take first step BAM FAIL!
Well maybe we do a form of genetic learning taking the best of partially successful attempts and glom them together in our brain to be skilled walkers.
Most robot walkers really suck ! They have giant feet and small legs. You could tear off a leg and turn the power off and they would still be standing (try that on a person). So, this is an attempt to make a better walker... it will still probably suck, but I won't know until I try it.
My design will have very small feet (maybe just rubber tipped dowels) and 4 - 6 Servos (cause that is how many I have), and accelerometer and a couple of touch sensors. Here is my first rough sketch.
References
I think there is an "Arty" component of Genetic Programming. The hard part is not turning the crank as with simple or defined algorithms. The hard part is defining the problem and encapsulating the success into something measurable.
I would be interested in assimilating the code into MyRobotLab framework. So I went looking around I found several implementations of Genetic Programming in Java.
This is what I've seen so far.
The experiments will run in stages. The first stage is to set some super simple experiments up with MyRobotLab, since I just got UDP video streaming to work. The video should be helpful in future for distributed Haar Training as mentioned previously.
More to come....
2010.12.14
http://frogleg.myrobotlab.org/ - starting to set up the experiment. I'd be curious if anyone gets to the video. If you do please let me know with a screenshot & lag time (I put a clock on the video). The filters are functional if you want to mess around. Don't know if I left out anything which blows stuff up... we'll see. Link to the Applet is at the bottom. Right now it is using UDP exclusively, this was an idea in order to make the video work for longer than 10 seconds. But I may need to go back and send only video on UDP and all other control messages on TCP.
For the record - this is what it looks like on my box.
You should see the clock update, and you should be able to change the OpenCV filters.
Lot's of shoulds.
2010.12.14.Later....
Something is goofy ... this was working (the client/server part) on different machines but now its not so ... don't waste time... I'll try to figure it out.
2010.12.18
As I mentioned before the challenge might not be the "programming" or framework of the system but defining the problem:
So here is my first stab at that definition.
Define the Problem
Find the Function that will send 2 Servos the correct data sequence to move the Frogs Foot in a predefined smooth "Walking Path"
The program does very well in generating a function to match the current (default) curve.
I through in a large sharp spike to see what would happen. After 500 iterations it failed to match the spike. I'm not really surprised as I believe it was constructed with the idea of following more smooth curves. And the goal which I am interested in is a smooth curve. The reason I did a spike is that was the easiest data I could tweak artificially.
2010.12.19 - Easy Filter - Frog Hunting at Night
I found the appropriate "green" LED and applied the following filters. Have not mounted LED to FrogLeg yet. As Gareth had mentioned I could possibly be doing this with a wii remote, but I have not purchased the PC bluetooth dongle. Getting a robust tracking was pretty easy with the lights off.
Now, I just need to save the coordinate positions off, scale them < 1, and put them in the applet...
Captured Froggy - is really a test of the motion capture. I used my hand to generate the motion to test the basic process. Also to test the export format of the data being captured was correct and in the range of data expected by the GP Applet.
Solving For Froggy - the red line represents the path of the LED with my hand. It came out relatively well. There was a bit of loop and other garbage at the end which I trimmed off. So it did not take long (131 iterations) for the GP Applet to solve for a nice curve - certainly nicer than my jiggly hand movements (too much coffee!)
What the graph and the formula represent is a simple X Y plot.
At the moment this would equate to a linear actuator Y and time X. As time proceeds a simple formula could be used to move the linear actuator up and down to follow the curve.
The Arduino'ish code snippet might begin to look like this:
int X; int Y;
for (X = 0; X < paceMax; ++X)
{
Y = "the generated formula" (X)
analogWrite(legPin, Y);
delay(delayAmount);
}
Next Steps:
So I think the next steps would be to actually mount the LED to froggy's foot. There is no linear actuator but 2 servos (Polar coordinates hopefully avoided by the whole GP thing). Another task would be to incorporate the GP into MyRobotLab... always more steps for froggy foot.... hop hop hop...
Trying to assimilate Frog GP into MyRobotLab... you will be assimilated... Riddipp... Riddipp...
5 Legged Horse
Here is an example of GP finding a solution but not necessarily the optimal one. A 137 node formula was used to derive the graph Y = 0.5
The Simplest Case
Here the input data was a single point (0.5, 0.5).
The Genetic Program of fitness 1.0 (perfect) was created in the first generation.
As you can see the program evaluated to Y = (X - X) + X, which is Y = X .... Yay !
2010.12.20 - GP Code Assimilated
Some of this stuff is now starting to congeal. First in order to generate a program you need a pool of possible nodes.
This will be the pool of my proposed nodes for the first simple tests. It is what Gerber's Applet had with the addition of (Move Servo #1 (value) & Move Servo #2 (value)).
The other work which will need to occur is to get the feedback of the LED position to interact with the Fitness Function / evaluation.
Pool Of Possible Nodes - to create a Genetic Program
Possible Solution
This possible solution would get evaluated with the feedback for the fitness function coming from the LED position
2010.12.26 - Feedback enabled
It doesn't look like the camera capture is following the leg in the video, but that has to do with frames dropping in the screen capture program.
Here is the result after 43 generations. It follows the grandfather program (from generation 36) pretty closely. I can see mutants appear occasionally. I think they "look better" than grandfather#36. There is some "adjusting" on fitness values which now may not be appropriate. In addition the programs trees have grown to about 150 nodes, with ~120 calls to the servo in evaluating 1 program. The calls are not being processed because the ~120 calls to the servo make no relevant changes. They basically overrun the servo. I was thinking in modifying the program tree building functions so that there could be only 1 call to each servo (knee & hip). The actual values can be modified 120+ times, but it will only be 1 call to each servo.
Telefox and Rik mentioned the amount of "useless" material in DNA, but I think there is plenty diversity for the values of the servos versus the actual call to the servos. For example moveKnee( (x + 10) - 10) would still be possible or moveHip ((x + 0) - 1) + 1) , moveHip(x / x * x ) ... etc So, there is plenty of room to create garbage.
2010.12.27 - A few goofy things
I think its still a good routine - the part I miss calculated was the fact that even though the program commands the servos to move the delay between when they are actually moving and when the servos get the command is enough time to snap 2 stable pictures.
I will add a delay before checking the video stream, to at least get the servos "moving"
2010.12.28 - Found goofy thing behind keyboard
There was a nagging feeling I had previously regarding setting the servos to an initial state. It turned out the nagging feeling was correct. I stepped through the code, when a "Best" was selected which had a single knee move. That would mean the program was completely dependent on some previous individual setting the hip ! Ooops!
2010.12.29 - Summary
Genetic Programming is very appealing because of its organic design.
Unfortunately, I needed to slow the video capture / genetic processing loop down to 1 second per iteration. This was necessary to capture accurate positions of the LED. There are 4 points of evaluation in each Individual fitness test. It is also necessary to set an initial state, for each individual. This comes to 5 seconds per each evaluation. So if we would like to do 999 generations at 100 individuals each with 5 seconds per evaluation, the time involved would be nearly 6 days ! I don't have the patience. Although it was fun to walk away from the computer, and have it busily "crunching" possible solutions. But, the servo noise was quite annoying.
It is important with genetic programming the evaluation of each individual program happens very quickly, otherwise the time needed to create something useful becomes way too long.
Genetic programs working in simulations can work considerably faster, but at a possible cost of not modeling the real environment satisfactorily. Also, creating or interfacing with a simulator may not be worth the effort in some situations. Telefox, shared some links on a project which has done genetic programming/simulator integration. The Starfish robot uses a simulator to run through possible walking modes - here is an example.
The following images are the results of letting the program and feedback system process for 1/2 a day. Not spectacular, but not bad either. I now would like to try something with accelerometer feedback. The feedback system of the accelerometer should be considerably faster than the video system. Balancing should be challenging. The amount of time balanced should be part of the fitness program. I've been kicking around a few ideas but nothing concrete, and now i want to spend some time on WiiDar (wii LIDAR) since Santa gave me a blue-tooth dongle.
I would welcome feedback, or anyone wanting to experiment with a similar setup.
Series of screenshots from 30 generations of the GP trying to find a program
who's optimal solution was the yellow trapezoid. The red letters & red path represent the
current best. The grey text and grey path represent the current individual program
being evaluated.
myrobotlab