Recurrent Neural Network for MoonBoard Climbing Route Classification and Generation
This work provides a more accurate method for predicting climbing route difficulty and generating new routes, which could benefit climbers and route setters by offering better training and route creation tools.
This paper tackles the challenge of classifying and generating climbing routes on a MoonBoard. By preprocessing climbing routes into move sequences using a novel pipeline called "BetaMove", the authors achieved near human-level performance in route grade prediction and generated new routes of significantly higher quality compared to previous methods.
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.