Autonomous robot navigation in unstructured outdoor environments remains a critical challenge for tasks such as reconnaissance, search and rescue and automated driving. This workshop addresses two main components necessary for formulating open problems in outdoor navigation within the theoretical framework of Machine Learning. The first is concerned with using color cameras as the primary sensing modality for learning models of traversable terrain over the long term. The second is concerned with learning the necessary locomotion required to allow legged robots to efficiently move through rough outdoor terrain.