Embodied navigation methods commonly operate in static environments with stationary targets. In this work, we present a new algorithm for navigation in dynamic scenarios with non-stationary targets. Our novel Transit-Aware Strategy (TAS) enriches embodied navigation policies with object path information. TAS improves performance in non-stationary environments by rewarding agents for synchronizing their routes with target routes. To evaluate TAS, we further introduce Dynamic Object Maps (DOMs), a dynamic variant of node-attributed topological graphs with structured object transitions. DOMs are inspired by human habits to simulate realistic object routes on a graph. Our experiments show that on average, TAS improves agent Success Rate (SR) by 21.1 in non-stationary environments, while also generalizing better from static environments by 44.5% when measured by Relative Change in Success (RCS). We qualitatively investigate TAS-agent performance on DOMs and draw various inferences to help better model generalist navigation policies. To the best of our knowledge, ours is the first work that quantifies the adaptability of embodied navigation methods in non-stationary environments. Code and data for our benchmark will be made publicly available.

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