提出新基准NavSpace，评估导航智能中的空间感知与推理能力
NavSpace: How Navigation Agents Follow Spatial Intelligence Instructions
- 设计六类任务、1228组轨迹指令对，系统评估空间智能
- 22个模型在该基准上表现参差，暴露现有模型短板
- 提出SNav模型，在仿真与真实机器人测试中均领先
指令跟随导航是实现具身智能的关键步骤。以往基准主要关注语义理解，忽视了对导航智能体空间感知与推理能力的系统评估。本文提出NavSpace基准，包含六类任务和1,228组轨迹-指令配对，用于探测导航智能体的空间智能。我们在该基准上全面评估了22个导航智能体，包括前沿导航模型和多模态大语言模型。评估结果揭示了当前具身导航中的空间智能现状。此外，我们提出SNav，一种新的空间智能导航模型。SNav在NavSpace基准及真实机器人测试中均优于现有方法，为未来研究建立了强基线。
原文摘要 · Abstract (English)
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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