Bridging the Indoor-Outdoor Gap: Vision-Centric Instruction-Guided Embodied Navigation for the Last Meters

• URL: http://arxiv.org/abs/2602.06427v1
• Date: Fri, 06 Feb 2026 06:52:23 GMT
• Title: Bridging the Indoor-Outdoor Gap: Vision-Centric Instruction-Guided Embodied Navigation for the Last Meters
• Authors: Yuxiang Zhao, Yirong Yang, Yanqing Zhu, Yanfen Shen, Chiyu Wang, Zhining Gu, Pei Shi, Wei Guo, Mu Xu,
• Abstract summary: Embodied navigation holds significant promise for real-world applications such as last-mile delivery.<n>We introduce a novel task: out-to-in prior-free instruction-driven embodied navigation.<n>We propose a vision-centric embodied navigation framework that leverages image-based prompts to drive decision-making.
• Score: 5.804644839559736
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Embodied navigation holds significant promise for real-world applications such as last-mile delivery. However, most existing approaches are confined to either indoor or outdoor environments and rely heavily on strong assumptions, such as access to precise coordinate systems. While current outdoor methods can guide agents to the vicinity of a target using coarse-grained localization, they fail to enable fine-grained entry through specific building entrances, critically limiting their utility in practical deployment scenarios that require seamless outdoor-to-indoor transitions. To bridge this gap, we introduce a novel task: out-to-in prior-free instruction-driven embodied navigation. This formulation explicitly eliminates reliance on accurate external priors, requiring agents to navigate solely based on egocentric visual observations guided by instructions. To tackle this task, we propose a vision-centric embodied navigation framework that leverages image-based prompts to drive decision-making. Additionally, we present the first open-source dataset for this task, featuring a pipeline that integrates trajectory-conditioned video synthesis into the data generation process. Through extensive experiments, we demonstrate that our proposed method consistently outperforms state-of-the-art baselines across key metrics including success rate and path efficiency.

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