Embedding and generation of indoor climbing routes with variational autoencoder
This work addresses route setting for indoor climbing enthusiasts, but it is incremental as it applies an existing method to a new domain.
The authors tackled the problem of generating indoor climbing routes by using a variational autoencoder on MoonBoard data, resulting in 22 generated routes that were uploaded for user review.
Recent increase in popularity of indoor climbing allows possible applications of deep learning algorthms to classify and generate climbing routes. In this work, we employ a variational autoencoder to climbing routes in a standardized training apparatus MoonBoard, a well-known training tool within the climbing community. By sampling the encoded latent space, it is observed that the algorithm can generate high quality climbing routes. 22 generated problems are uploaded to the Moonboard app for user review. This algorithm could serve as a first step to facilitate indoor climbing route setting.