AI导航新思路：把思考和控制拆开，效果飙升
现在的视觉语言导航模型，往往把“看路”和“走路”混在一起，结果容易迷路、听不懂长尾指令，而且像个黑箱。这篇论文反其道而行之：把思考和控制拆成两个模块。慢速的视觉语言推理器先做显式的链式思考，输出一个“像素目标”——就是图像上的一个点，作为通用接口。然后快速的动作专家根据这个点和文本指令，生成连续的路径点。这样，高层意图和底层控制通过像素锚点和语言痕迹连接起来。在城市级导航中，POI到达率提升35%达到77.3%，室内外场景成功率分别达95.4%和92.9%。它不是你明天能用上的，但为可解释、可泛化的导航模型提供了新范式。
📄 原文摘要(英文)
Visual Language Navigation foundation models aim to unify deep reasoning for grounded spatial decisions with broad versatility for diverse embodied tasks. Current approaches typically achieve this integration via monolithic policies that map observations directly to actions, yet they often suffer from coordinate drift and poor handling of long-tail semantics. Furthermore, these black-box mappings lack interpretability, hindering the simultaneous achievement of generality, robustness, and transparency. We present ABot-N1, a step toward a general Visual Language Navigation foundation model, that addresses these challenges by decoupling cognition from control via a slow-fast architecture guided by dual visual-language signals. More specifically, a slow vision-language reasoner performs explicit Chain-of-Thought reasoning while producing a pixel goal. This compact set of image-space anchor points serves as a universal interface for diverse tasks, including point-goal, object-goal, poi-goal, instruction-following, and person-following. Subsequently, a fast action expert leverages both the textual cues and the pixel guidance to generate continuous waypoints at the native control frequency. By bridging high-level intents and low-level control through pixel-grounded anchors paired with explicit linguistic traces, our approach ensures robust, generalizable, and interpretable navigation across simulation and real-world benchmarks. ABot-N1 establishes new state-of-the-art records, delivering massive gains specifically in urban-scale navigation: boosting POI arrival by 35.0% (to 77.3%) and achieving 95.4%/92.9% SR in complex indoor and outdoor scenes. It also maintains superior robustness across object-reaching, person-following, and instruction-following tasks. New Point-Goal/POI-Goal benchmarks are released as open source to advance the field of urban-scale navigation.