  Classifying the difficulties of climbing routes and generating new routes are
both challenging. Existing machine learning models not only fail to accurately
predict a problem's difficulty, but they are also unable to generate reasonable
problems. In this work, we introduced "BetaMove", a new move preprocessing
pipeline we developed, in order to mimic a human climber's hand sequence. The
preprocessed move sequences were then used to train both a route generator and
a grade predictor. By preprocessing a MoonBoard problem into a proper move
sequence, the accuracy of our grade predictor reaches near human-level
performance, and our route generator produces new routes of much better quality
compared to previous work. We demonstrated that with BetaMove, we are able to
inject human insights into the machine learning problems, and this can be the
foundations for future transfer learning on climbing style classification
problems.
