让机器人用“指一下”代替“想半天”
现在的机器人导航，每换一个任务、换一台机器，就得重写一套感知和决策代码。这篇把这事拆了：它不教模型“怎么走”，而是让一个已经会看图说话的视觉语言模型，用“指方向”这个统一动作来表达意图——想去哪、追什么、找什么，都翻译成指向，再由一个动作编码器把指向变成具体机器人的轨迹。一个模型，2千多个场景、4千多小时数据训练，在10个公开导航模拟环境里全部拿到最高成功率，还能零样本直接换到真机上。它不是你明天能买回家的产品，但它指了一条路：机器人的“身体”和“脑子”可以分开，脑子用现成的，身体只负责执行。
📄 原文摘要(英文)
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.