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Recent increase in popularity of indoor climbing allows possible applicationsof deep learning algorthms to classify and generate climbing routes. In thiswork, we employ a variational autoencoder to climbing routes in a standardizedtraining apparatus MoonBoard, a well-known training tool within the climbingcommunity. By sampling the encoded latent space, it is observed that thealgorithm can generate high quality climbing routes. 22 generated problems areuploaded to the Moonboard app for user review. This algorithm could serve as afirst step to facilitate indoor climbing route setting.

Embedding and generation of indoor climbing routes with variational autoencoder