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Terrain traversability estimation is a fundamental task for supporting robot navigation on uneven surfaces. Recent learning-based approaches for predicting traversability from RGB images have shown promising results, but require manual annotation of a large number of images for training. To address this limitation, we present a method for traversability estimation on unlabeled videos that combines dataset synthesis, self-supervision and unsupervised domain adaptation. We pose the traversability estimation as a vector regression task over vertical bands of the observed frame. The model is pre-trained through self-supervision to reduce the distribution shift between synthetic and real data and encourage shared feature learning. Then, supervised training on synthetic videos is carried out, while employing an unsupervised domain adaptation loss to improve its generalization capabilities on real scenes …