GemNav: Discrete-Token Visual Robot Navigation using a Multimodal Large Language Model

Abstract

Visual navigation policies built on large pretrained models increasingly rely on large cross-embodiment datasets and expressive action-generation mechanisms. In this paper, we ask whether a pretrained Multimodal Large Language Model (MLLM) can be adapted efficiently for robot navigation by rethinking the representation and construction of navigation actions. We introduce GemNav, a visual navigation policy that represents each robot-local waypoint coordinate as a categorical distribution over discrete metric value bins and soft-decodes it into a continuous coordinate for execution. This probabilistic representation retains likelihood information over alternative coordinate values while providing a compact action space suitable for robot navigation. We further construct training trajectories using metric-distance goal sampling rather than fixed future frame offsets, exposing the policy to varying physical navigation horizons independent of dataset frame rate and robot speed. GemNav is trained on a single 8.7-hour public SCAND dataset, roughly three orders of magnitude smaller than competing cross-embodiment datasets. Extensive offline and closed-loop experiments show improved out-of-distribution generalization over frame-based sampling and substantially more reliable obstacle navigation than a matched continuous regression head. The same policy transfers without per-platform retraining across unseen environments and three physically distinct robot platforms.