GemNav: Discrete-Token Visual Robot Navigation using a Multimodal Large Language Model
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-...
| Authors | Peter Bohm, Saimunur Rahman, Abdelwahed Khamis et al. |
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| Primary category | cs.RO |
| Published | 8 July 2026 |
| Last updated at source | 26 July 2026 |
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