Reasoning about the Unseen for Efficient Outdoor Object Navigation
This addresses the challenge of enabling robots to navigate efficiently in unmapped outdoor settings, which is crucial for real-world applications, representing a novel extension beyond indoor-focused methods.
The paper tackles the problem of object goal navigation in unstructured outdoor environments by introducing a new task, a mechanism for LLMs to reason about unseen areas, and a new success metric, achieving impressive results on simulated and physical robots without pre-mapping.
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