Using Machine Learning for move sequence visualization and generation in climbing
Machine Learning
2025-03-04 v1 Computer Vision and Pattern Recognition
Abstract
In this work, we investigate the application of Machine Learning techniques to sport climbing. Expanding upon previous projects, we develop a visualization tool for move sequence evaluation on a given boulder. Then, we look into move sequence prediction from simple holds sequence information using three different Transformer models. While the results are not conclusive, they are a first step in this kind of approach and lay the ground for future work.
Cite
@article{arxiv.2503.00458,
title = {Using Machine Learning for move sequence visualization and generation in climbing},
author = {Thomas Rimbot and Martin Jaggi and Luis Barba},
journal= {arXiv preprint arXiv:2503.00458},
year = {2025}
}