[Ginto8]

Aside from the many technical limitations, there is one glaring barrier to such a learning system. Vision systems play very specific roles in each game, and in each robot. They typically tracking geometric, retroreflective targets, but the vision systems my team has created have had no say in the robot's logic -- they effectively turn the camera from an image sensor to a target sensor, streaming data about where the targets are back to the robot. For a vision system to learn the game, it must learn not only what the targets look like, but also what data the robot's central control needs -- whether it wants "is the target there?" data like this year's hot goals, or "At what angle is the target?" as in Rebound Rumble and Ultimate Ascent. Any learning system requires feedback to adapt, and when it has to learn so many different things, designing that feedback system would be at least as complex as making a new vision system, and certainly more error-prone.