The goal is for the mid point of the hand to be positioned on the white circle.
Fitness is measured over 28 defined target points (28 trials).
For each trial we calculate the mean distance from target across all timesteps, early timesteps are weighted down to de-emphasise speed approaching target and emphasise accuracy of targetting.
Final fitness is the square of each trial's mean distance value. This emphasises improving the worst trials over better ones (classic reduction of squared error)
Colin
2010-11-19
Here's a video of SharpNEAT evolving a controller to keep a player arm
straight.
Evaluation/fitness metric is hand height averaged over all timesteps.
A more direct metric would be to punish joint angle deviation from
zero degrees, hence there is /some/ degree of indirectness in the
fitness metric. E.g. if you just nudge the arm at the start and allow
it to collapse you can get the hand to rest on the arm base thus
giving it some height, thus giving rise to at least one very simple
local fitness maximum.
"This video shows some different progress with the AI Challenge. The previous video demonstrated how the computer was directly manipulating the joints through a neural network. In this video, there's a proportional controller attached to each joint, which only tries to get the angles of each joint to some particular value.
These values are set by a joystick that I attached to the computer.
[...]"