GemNav: Discrete-Token Visual Robot Navigation using a Multimodal Large Language Model
For robot navigation, this work shows that discrete-token adaptation of frozen MLLMs can be a data-efficient alternative to traditional navigation policies, though it is incremental as it applies existing LoRA and discrete token techniques to a new domain.
GemNav adapts a frozen Multimodal Large Language Model (MLLM) for visual robot navigation using only LoRA on the language tower, achieving zero-shot transfer to four unseen environments with goal distances of 0.25-0.42m, trained on just 8.7 hours of data—three orders of magnitude less than competing methods.
Visual navigation policies built on large pretrained models have so far followed a common recipe: a dedicated visual encoder, a bespoke action head, and training on thousands of hours of cross-embodiment datasets. We ask whether this recipe is necessary. In this paper, we introduce GemNav, a visual robot navigation policy that adapts a frozen Multimodal Large Language Model (MLLM) for short-to-medium horizon waypoint navigation using Low-Rank Adaptation (LoRA) on the language tower alone, with no auxiliary visual encoder and no continuous regression head. Waypoints and categorical navigation signals share a single discrete token vocabulary generated by the language-model head, and a soft-decoded auxiliary loss recovers the metric structure that pure cross-entropy training discards. On a single 8.7-hour open corpus, roughly three orders of magnitude smaller than competing training sets, the policy transfers zero-shot to four physically distinct unseen environments and stops within 0.25-0.42m of the goal across 20 real-world trials covering an open carpark, an obstacle carpark, a long outdoor chemical yard, and an indoor warehouse. Conditioning on short image histories improves offline metrics but yields no robot benefit, pointing to a ceiling on what temporal context adds once pretrained vision features are in place. These results indicate that discrete-token adaptation of frozen MLLMs can provide a data-efficient, deployable alternative for foundation model robot navigation.