Using Machine Learning for move sequence visualization and generation in climbing

• URL: http://arxiv.org/abs/2503.00458v1
• Date: Sat, 01 Mar 2025 11:50:36 GMT
• Title: Using Machine Learning for move sequence visualization and generation in climbing
• Authors: Thomas Rimbot, Martin Jaggi, Luis Barba,
• Abstract summary: We develop a visualization tool for move sequence evaluation on a given boulder.<n>Then, we look into move sequence prediction from simple holds sequence information using three different Transformer models.
• Score: 35.1762496625647
• License: http://creativecommons.org/licenses/by/4.0/
• 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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