Abstract
Object goal navigation requires an embodied agent to locate and reach target objects in complex environments. Humans often rely on low-cost yet informative schematic maps, such as rough sketches or directional annotations. We develop a workflow for schematic-guided embodied navigation with two components: a training-data generation pipeline and ScheNav, a closed-loop navigation framework. Our lightweight data-generation pipeline turns 2D floorplans into navigation training environments, yielding Furnished-RPLAN, a dataset of over 33k furnished layouts with paired navigation episodes and noise-controllable schematic maps. ScheNav turns rough schematic guidance into exploration and stopping decisions by using SchemaField, a learned spatial module that fuses the guidance with accumulated observations and predicts spatial value fields. The framework updates these fields as exploration proceeds, with trajectory-based training and a cross-frame consistency objective supporting coherent guidance across changing viewpoints. ScheNav learns to use schematic guidance entirely from simulated floorplans and transfers this ability to unseen embodied environments. Experiments show improved navigation success and path efficiency across schematic maps, goal ellipses, and pointed directions, together with tolerance to the tested schematic perturbations and omissions. Together, these results demonstrate that rough schematic guidance offers a practical way to improve embodied navigation.
Abstract (translated)
URL
https://arxiv.org/abs/2610.34286
