Personalized Embodied Navigation for Portable Object Finding

ArXi:2403.09905v5 Announce Type: replace-cross Embodied navigation methods commonly operate in static environments with stationary objects. In this work, we present approaches for tackling navigation in dynamic scenarios with non-stationary targets. In an indoor environment, we assume that these objects are everyday portable items moved by human intervention. We therefore formalize the problem as a personalized habit learning problem. To learn these habits, we