Quote:
Originally Posted by Ginto8
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.
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The two tasks you just described are in themselves not difficult to achieve through a vision program (An example method to do this is called cascade training), the real problem is how the robot would act on it. This task would be a no brainer for Yash, in fact, he has already done it for the 2014 game if I remember correctly. This only looks at one aspect of the game though. It also has to know what is in front of it, find game pieces, know whether it has game pieces, and go to where it needs to to score or pass. We did most of this in our code this year with 3 cameras and we were lucky to get 10 fps. It would take months at least for there to be enough generations of the learning algorithm for there to be any noticeable result.
Quote:
Originally Posted by MatthewC529
Its an awesome idea and you should definitely follow through but probably not immediately on a 120 lbs. robot. Experiment first with Game Algorithms and get used to implementing it in an efficient and workable way, then move to the robot where efficiency will really matter. I cant speak for how efficient you will need to be... again... Game Developer but again I really like your concept of pixels but I think you should be wary of how much time and the maintainability of your code.
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Isn't there a simulation for each year's game? In my mind, that would be a perfect place to start.