Recurrent Neural Network for MoonBoard Climbing Route Classification and Generation

Autor:	Duh, Yi-Shiou, Chang, Ray
Rok vydání:	2021
Předmět:	Computer Science - Machine Learning Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: 9 pages, 4 figures, 4 appendix figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2102.01788 Zobrazit plný text záznamu View this record from Arxiv