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

在室内、室外甚至未详细规划的环境中，机器人应存在。然而，最近在物体目标导航（OGN）方面的进展主要集中在室内环境中，利用空间和语义线索进行导航，这些线索在室外环境中并不具有普适性。本文介绍了一项新的任务 “OUTDOOR”，提出了一种新的机制，使大型语言模型（LLM）能够准确地幻想可能的未来，并提出了一种在这个更复杂领域中推动研究的计算感知成功度量。此外，我们还展示了在模拟无人机和实际四足机器人在户外环境中取得的令人印象深刻的结果。我们的代理机器人不需要预先地图，并且我们的形式化方法优于朴素的基于 LLM 的方法。