Learning provides a powerful tool for vision-based navigation, but the capabilities of learning-based policies are constrained by limited training data. If we could combine data from all available sources, including multiple kinds of robots, we could train more powerful navigation models. In this paper, we study how goal-conditioned policies for vision-based navigation can be trained on data obtained from many distinct but structurally similar robots, and enable broad generalization across envir…