  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.
