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arXiv · 2503.00458

Using Machine Learning for move sequence visualization and generation in climbing

Thomas Rimbot·Martin Jaggi·Luis Barba
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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.

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Thomas Rimbot, Martin Jaggi, Luis Barba. 2025-03-01. Using Machine Learning for move sequence visualization and generation in climbing. https://arxiv.org/abs/2503.00458

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