# Using Machine Learning for move sequence visualization and generation in climbing

arXiv: https://arxiv.org/abs/2503.00458 · HTML: https://mlsanity.com/paper/2503.00458/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 10 references that are in the map.
- First posted on arXiv 1 Mar 2025.

## Builds on (most cited first, 1 of 1)

- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)

## Abstract (arXiv)

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.