Using Machine Learning for move sequence visualization and generation in climbing

Using Machine Learning for move sequence visualization and generation in climbing Thomas Rimbot, Martin Jaggi, Luis Barba - EPFL 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. I NTRODUCTION Applying Machine Learning techniques to competitive sport has been an increasing trend in the past few years. We can for example cite the case of car racing or hockey. In this project, we focus on bouldering, a form of rock climbing where athletes are tasked with overcoming a small natural or artificial feature (about 4m high), requiring both physical strengths and problem-solving skills.