In high school I did this project-paper-thingy about NN, but it wasn't about robot vision. It was about image compression. A la take a typical 128-32-4-32-128 feed forward network and train it so it would output the same thing as was input to it. After it has converged, first three layers (128-32-4) make the encoder and 4-32-128 is the decoder and activation from middle 4 neurons represent the compressed image. Here are a couple of things I learned:
1) NN is no magic tool. It will do whatever you ask for it as best as it can, but no more. Most of the time it will give you an average solution compared to using conventional techniques.
2) It is VERY computationally intensive. Especially when we are talking about image processing with any reasonable resolution. Floating point math and nonlinear transfer functions is a never-ever on a MCU.
Still, it is pretty awesome that you can teach a robot to do certain things without changing a line of code. Feels kinda the right way to go. Also I would recommend sending the image from camera to a computer who does the learning/computation and then sending back the commands to the robot.
So, yeah, it's definitely an interesting project to do. It would be real nice if you could keep us posted on your progress.
Amm, out of curiosity, what kind of output are you training it to give you ?