VGN: Value-Guided Navigation from RGB for Robot Motion Planning

Visual navigation from monocular RGB observations remains challenging due to the need for reliable scene understanding and safe decision-making in diverse real-world environments. Existing modular approaches often depend on additional sensing or interaction-heavy pipelines for reliable traversability estimation, while end-to-end methods typically infer navigation-relevant structure indirectly through trajectory-centric supervision. Motivated by these observations, we propose Value-Guided Navigation (VGN), an RGB-based framework for learning dense traversability-aware and goal-conditioned planning representations for downstream navigation. To achieve this, we leverage vision foundation models together with classical planning algorithms to generate dense traversability and cost-to-go pseudo-labels directly from static RGB images. This supervision is generated without trajectory supervision or robot interaction and is used to train the student planning representation model. The learned representation is planner-agnostic and can be integrated with multiple downstream planning mechanisms. Experiments across multiple outdoor datasets, closed-loop simulation environments, and real-world deployment on a Unitree Go2 robot demonstrate effective RGB-based traversability estimation and local motion planning.