NavSpace: How Navigation Agents Follow Spatial Intelligence Instructions

• URL: http://arxiv.org/abs/2510.08173v1
• Date: Thu, 09 Oct 2025 12:59:19 GMT
• Title: NavSpace: How Navigation Agents Follow Spatial Intelligence Instructions
• Authors: Haolin Yang, Yuxing Long, Zhuoyuan Yu, Zihan Yang, Minghan Wang, Jiapeng Xu, Yihan Wang, Ziyan Yu, Wenzhe Cai, Lei Kang, Hao Dong,
• Abstract summary: We introduce the NavSpace benchmark, which contains task categories and 1,228 trajectory-instruction pairs.<n>We comprehensively evaluate 22 navigation agents, including state-of-the-art navigation models and multimodal large language models.<n>We propose SNav, a new spatially intelligent navigation model.
• Score: 31.144783513493433
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Instruction-following navigation is a key step toward embodied intelligence. Prior benchmarks mainly focus on semantic understanding but overlook systematically evaluating navigation agents' spatial perception and reasoning capabilities. In this work, we introduce the NavSpace benchmark, which contains six task categories and 1,228 trajectory-instruction pairs designed to probe the spatial intelligence of navigation agents. On this benchmark, we comprehensively evaluate 22 navigation agents, including state-of-the-art navigation models and multimodal large language models. The evaluation results lift the veil on spatial intelligence in embodied navigation. Furthermore, we propose SNav, a new spatially intelligent navigation model. SNav outperforms existing navigation agents on NavSpace and real robot tests, establishing a strong baseline for future work.

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