Robots should exist anywhere humans do: indoors, outdoors, and even unmapped environments. In contrast, the focus of recent advancements in Object Goal Navigation(OGN) has targeted navigating in indoor environments by leveraging spatial and semantic cues that do not generalize outdoors. While these contributions provide valuable insights into indoor scenarios, the broader spectrum of real-world robotic applications often extends to outdoor settings. As we transition to the vast and complex terrains of outdoor environments, new challenges emerge. Unlike the structured layouts found indoors, outdoor environments lack clear spatial delineations and are riddled with inherent semantic ambiguities. Despite this, humans navigate with ease because we can reason about the unseen. We introduce a new task OUTDOOR, a new mechanism for Large Language Models (LLMs) to accurately hallucinate possible futures, and a new computationally aware success metric for pushing research forward in this more complex domain. Additionally, we show impressive results on both a simulated drone and physical quadruped in outdoor environments. Our agent has no premapping and our formalism outperforms naive LLM-based approaches

翻译：机器人应存在于人类活动的任何场所：室内、室外，甚至未知环境。然而，近年来物体目标导航（Object Goal Navigation, OGN）的研究重点聚焦于利用难以泛化至室外场景的空间与语义线索在室内环境中的导航。尽管这些研究为室内场景提供了宝贵见解，但现实机器人应用的更广泛场景常延伸至室外环境。当转向广袤而复杂的室外地形时，新挑战随之显现。与室内结构化布局不同，室外环境缺乏明确的空间分界，且充满固有的语义歧义。尽管如此，人类仍能轻松导航，因为我们能对未知场景进行推理。我们提出了一项名为OUTDOOR的新任务、一种使大型语言模型（LLMs）精准推理可能未来情景的新机制，以及一项推动该复杂领域研究的新计算感知成功度量。此外，我们在模拟无人机和实体四足机器人的室外环境中展示了令人瞩目的成果。我们的智能体无需预建地图，且所提出的形式化方法优于基于LLM的朴素基线方案。