LightNav-0：激發VLM空間智能以實現通用具身導航

摘要

具身導航要求智能體在不同的任務、環境與機器人形態之間，將異質目標與視覺觀測轉化為行動。現代視覺語言模型（VLM）已編碼了支援視覺定位、空間推理與指向的空間先驗，但這些能力很少被直接引出用於機器人控制。現有導航系統反而依賴任務或形態特異的組件，將感知、推理與行動分割開來，僅提供有限的泛化能力。在此，我們提出 LightNav-0，一個緊湊的通用型具身導航模型，它激發預訓練 VLM 的空間智能並將其與導航對齊，無需任務特異的預測頭。LightNav-0 透過統一的 token 介面表示多樣化的導航任務：雙通道指向表達與任務、場景及形態無關的空間意圖，而殘差向量量化動作 tokenizer 則將此意圖映射為精確且特定於形態的軌跡。結合具時間感知的視覺歷史壓縮、具身推理中間訓練、監督微調與強化學習，此設計使單一模型即可支援指令跟隨、開放詞彙物體導航與視覺追蹤。該導航訓練語料庫涵蓋 2,000+ 個場景與 4,000+ 小時的具身導航資料。用於初始化 LightNav-0 的具身推理檢查點 LightNav-ER，在 8 個具身推理基準上取得最高的完整基準集平均；而 LightNav-0 在所有 10 個公開導航模擬設置中均達成單目成功率的最先進成果。真實世界評估更進一步展示了在跨機器人形態、多樣場景及靜態與動態目標上的零樣本泛化能力。這些結果確立了緊湊型 VLM 作為通用型具身導航之統一且可遷移骨幹的地位。

English

Embodied navigation requires agents to translate heterogeneous goals and visual observations into actions across tasks, environments, and robot embodiments. Modern vision-language models (VLMs) already encode spatial priors for visual grounding, spatial reasoning, and pointing, but these capabilities are rarely elicited directly for robot control. Existing navigation systems instead rely on task- or embodiment-specific components, fragmenting perception, reasoning, and action while offering limited generalization. Here we present LightNav-0, a compact generalist embodied navigation model that elicits the spatial intelligence of a pretrained VLM and aligns it with navigation, without task-specific prediction heads. LightNav-0 represents diverse navigation tasks through a unified token interface: dual-channel pointing expresses task-, scene-, and embodiment-agnostic spatial intent, while a residual vector-quantized action tokenizer maps this intent to precise, embodiment-specific trajectories. Together with temporally aware visual history compression, ER mid-training, supervised fine-tuning, and reinforcement learning, this formulation supports instruction following, open-vocabulary object navigation, and visual tracking within a single model. The navigation training corpus spans 2K+ scenes and 4K+ hours of embodied navigation data. LightNav-ER, the embodied-reasoning checkpoint used to initialize LightNav-0, attains the highest complete-set average across 8 embodied-reasoning benchmarks, while LightNav-0 achieves state-of-the-art monocular success rates across all 10 public navigation simulation settings. Real-world evaluations further demonstrate zero-shot generalization across robot embodiments, diverse scenes, and static and dynamic targets. These results establish compact VLMs as a unified and transferable backbone for generalist embodied navigation.