[gblake]

My hunch is that accomplishing the original post's goal involves solving (or integrating the solutions to), not dozens, but a few hundreds of problems, and my hunch is that converting the vision system's raw imagery into useful estimates of the states of the important objects involved in a match will be the hardest part.

To help wrap your head around the job(s) imagine the zillions of individual steps involved in carrying out the following ...

Create a simple simulation of the field, robots, and game objects for any one year's game.

Use the field/objects/robots simulation to simulate the sensor data (vision data) an autonomous robot would receive during a match. Be sure to include opponents actively interfering with your robot.

Add in the internal-state data the robot would have describing its own state.

Then - Ask yourself, what learning algorithms do I apply to this and how will I implement them.

It's a daunting job, but, if folks can get Aibo toy dogs to play soccer, you could probably come up with a (simulated initially?) super-simple robot that could put a few points on the board during a typical FRC match.

It's veerrryyyy unlikely that an implementation will make any human opponents nervous in this decade (most other posters have said the same); but I think what the OP described can be made, if your goal is simply playing, and your goal isn't winning against humans.

Blake