Using Machine Learning for move sequence visualization and generation in climbing

2025-03-01 · Thomas Rimbot, Martin Jaggi, Luis Barba

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.

📄 PDF Abstract BibTeX arXiv:2503.00458

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