Visuomotor robot arm coordination : Step 2 – Build the model

At the previous step I collected data using my robot arm and the webcams I installed on it. The data contains the detected object coordinates on the left and right image, the robot arm joint angles where it detected the object and the same variables but when the arm is placed manually on the object.

So, for each detected object (i.e. database record), I recorded these values we have this :

  • Position of object on the left image (xL, yL)
  • Position of object on the right image (xR, yR)
  • Arm joint angles where the object has been seen : (alphaI, betaI, thetaI, gammaI)
  • Arm joint angles at the object location : (alphaT, betaT, thetaT, gammaT)

Now the goal is to build a model that will predict the arm joint angles that will place the arm at the location of the object. To make this prediction, the model inputs will be the object position on each image and the current arm joint angles.

I started by trying to analyze the data I gathered to determine its properties. I quickly found that the data won’t be linear, so that I’ll need a non linear model to map the data. I’m used to neural networks, they are powerful non linear models, so I decided to implement a framework that will help me make the model learn the data.

The learning process

To ease the learning process using the DeMIMOI models, I decided to proceed in 4 steps :

  1. Prepare and preprocess the data to be learnt
    The idea is to build a specific dataset out of the raw data. For example if I have to make a mathematical operation on the raw data, say a subtraction or even a sine.
    The resulting data is stored in a temporary storage (the PC RAM in my case).
  2. Teach the model
    The learning algorithm uses the temporary storage to feed the model with the data to learn.
  3. Plot the results of the obtained model
    At this step, you want to see what the model learnt, so you just plot the output of the model and the desired output together to see the error.
  4. Save the model
    Once the model fulfill the needs, it can be saved for a later use.

Learning process implementation

For the first three steps, I created three DeMIMOI_Collection that each holds the required elements of each step.
Now I’m going to show you each of them and describe what’s going on.

  1. Data preparation and preprocessing
    Visuo Motor Coordination - Data PreprocessingThis step requires raw data. The data comes from the Long Term Memory block which hides a MongoDB database. The raw data flowing out of this block is then directly fed to the STM (Short Term Memory) block. On this particular case, I could have sent the raw data directly to the model,but I needed to make some calculations before, which where then removed…
  2. Teaching the model
    Visuo Motor Coordination - Learning StepThis step is the learning step. The data created at the previous step is fed to the Neural Network learning algorithm. Before sending the data to this block, we must make some data scaling since neural networks need [0, 1] or [-1, 1] normalized data depending on the activation function. This is why I put two normalization blocks, one on the inputs and the other one on the outputs.
    On this picture, we clearly see what are the inputs and the desired outputs we want the model to learn.
  3. Plotting the results
    Visuo Motor Coordination - Data PlottingOnce the model is built and ready to operate, I want to see the results of the learning step. I reuse the STM data as input, the data normalizer to ensure data normalization, then the neural network gets normalized data, that is in turn normalized back to output units. Finally this data is plotted by the DeMIMOI_Chart0 that is mapped to the Windows Forms Chart control I placed on the form.
    This results to this interface :
    Visuo Motor Coordination - Application Learning StepWe can see that for each graph, the neural network outputs are really close to the desired outputs, which means that it converged to a « good » solution.
    The left textbox contains Graphviz code to draw the system structure I showed all along this article. The right one contains the Graphviz neural network structure.

Now, it seems that we managed to get a model that is able to predict the end effector position to place it on the object that has been seen. So now, that’s the exciting part : let’s build the application that will demonstrate this ability in real time ! It will be the subject of my next post !

Some news on my DeMIMOI library

First of all, I wish everyone coming to this blog a happy new year ! Should this year be healthy and make your projects come true !

It’s been a while now since I published the code of my DeMIMOI core library. I had some time to make some improvements and add some more features to make it even more attractive to me, so possibly for anyone willing to give it a try too.

I thought well when I hoped this platform would help me easily build systems and make a bigger one by connecting smaller ones. I successfully managed to use the library with neural networks (special thanks to Accord.Net and AForge.Net). For example I made DeMIMOI models of a NARX (Nonlinear AutoRegressive eXogenous) neural network and a ARX regression model (AutoRegressive eXogenous).
These models can mimic almost any dynamic system mostly because they have a backward link that make their outputs at time t-1 one of their inputs.
Using the DeMIMOI library this backward link is easy to model and to code since it’s just a connection between an input and an output that, in code, is translated by a myModel.Outputs[0][0].ConnectTo(myModel.Inputs[0][0]) for example. The data flow is then automatically managed by the DeMIMOI core.

I also started to put a database manager in a DeMIMOI model which can currently connect to a Mongo database. So I can save and retrieve data to build models or to act as some kind of a memory… Well it’s still in development now but the main features are already up ! I mean at least reading data from a pre-existing database. One step at a time right ?

To give you a better idea of what I’m talking about and what I’m doing, I’ll show you a picture of the system I’m currently working on.

First of all, let me just explain the background.

While coding the DeMIMOI ARX and NARX models, I wanted to build models that can learn the behavior of the servomotors of my AL5C robotic arm.
On a previous attempt last year, I had the arm moving freely and randomly while an application was recording at each time step the angle values from the potentiometers of each servo (thank you so much Phidgets !).

The results have been stored in a database that I can read using my DeMIMOI memory manager. Those data can then be sent to the ARX and NARX models, and also be used by the learner models to fit them to the servo behavior.

For this purpose, I coded the system which is described by the following diagram :

DeMIMOI NARX and ARX learning Lynxmotion AL5C servoBy the way, this image has been created using the GraphViz code that is automatically generated by the DeMIMOI collection that holds all the blocks of the system.
That’s a feature I’m quite proud of since it allows me to quickly check if what I coded is what I expected the system to be !

On the left of the diagram is the memory manager that reads the servo values from a Mongo database. The data produced is then fed to the ARX model and its teacher. They don’t need extra data processing, as opposed to the NARX model.

Indeed, the NARX model is made of a neural network that needs normalized data. That’s why you can see that the data coming from the memory block is also fed to a normalizer block that converts the raw data to a [-1, 1] range before being sent to the NARX and its teacher. Then the data coming out of the NARX model is denormalized to revert it back to its original data space.

On the ARX and NARX models you can clearly see the backward link that connects the output to the input. This link makes the network seamlessly recurrent. And again, in term of coding, it’s nothing harder than creating the link !

You also may have noticed the three probes that display some data values. This was for me to quickly check what are the model ouputs (simulated) compared to the real output (measured on the real system). On that run, the ARX is better than the NARX, but I currently didn’t push any analyze further to explain this…

My next work will now be focused on analyzing the results deeper, maybe working on the so called BPTT neural network (ouch ! it hurts !) or maybe even trying to make some kind of automated learning shell that would be able to test multiple models and parameters and then select the best one…
I know ! It seams like there’s going to be a huuuuge mountain to climb… I fight this feeling by telling me that I already climb quite a big part that it would be even worse to stop now !

I’ll let you know of my progress in a while. And do not hesitate to say hi or comment on this ! I’d be curious to know what it feels like for someone external !

Delayed Multiple Input Multiple Output Interface library

On my last post, I talked about the « connectionist theory base models ».
I started coding a C# library that could help me in my AI projects in a sense that I keep on spending time putting « small » systems together to make a bigger and smarter one. It’s been some time that I thought it must exist some way to ease things up…

The initial idea is based on a feeling I have around a lot of systems I have to deal with at school, at work, in my everyday life, in programming… : modularity.

They all have common characteristics, but one among them is a major one : the all have either input(s) or output(s) or a combination of the two.
They all have a function which make them useful and working.
You can easily think of such systems like :

  • Your coffee machine
    • Function : make coffee
    • Input : coffee, water and energy
    • Output : drinkable coffee
  • Your DVD player
    • Function : read a DVD and generate appropriate video signals out of it
    • Input : DVD and energy
    • Output : TV compatible video signals

They have a second similarity : they may need to know what happened in the past. Some of the actions of a system may depend on what it did before or on what were the previous inputs.

Another important characteristic is that those systems can be and most of the time are connected to another system. In the previous example of the DVD player, what would be the goal of a DVD player if it could not be connected to a TV ?

These are the major point I kept in mind to start coding the library.
Finally after some iterations and some diverse structure trials I finally came to a satisfying system that allows to mimic all the points I talked about combined with an ease of use in term of programming.

If you are interested in the details or want to try/use this C# library, I published it on GitHub :
I called it DeMIMOI which stands for Delayed Multiple Input Multiple Output Interface, in reference to the control theory denomination.

At this time I did not use the library in an AI context but I already tried the DeMIMOI library for signal filtering for example. The use case is provided as a sample in the GitHub repository.

I’m pretty sure this is going to help in my experimentations since I’ll be able to build and test reasonably small parts of a system and then simply connect it to a bigger system to include this new module.

The other interesting part is that I’m more on the connectionism theory regarding AI, so I think it will help me make connection based systems easily.