EffiNav: Fusing Depth and Vision-Language for Efficient Object Goal Navigation

📄 arXiv: 2606.18634v1 📥 PDF

作者: Zecheng Yin, Benedict Jun Ma

分类: cs.RO, cs.AI

发布日期: 2026-06-17

💡 一句话要点

提出EffiNav以解决高效目标物体导航问题

🎯 匹配领域: 支柱三：空间感知与语义 (Perception & Semantics)

关键词: 目标物体导航 深度与视觉融合 自主智能体 路径优化 环境感知 机器人导航

📋 核心要点

  1. 现有的目标物体导航方法在泛化能力和效率上存在不足，导致过度探索已访问区域或冗余的往返运动。
  2. EffiNav通过融合深度信息和视觉语言，优化导航路径选择，从而提高探索效率。
  3. 在Habitat Matterport 3D和Open-Vocabulary Object目标导航基准上，EffiNav的表现与最新基线相当或更优，显示出其高效性和鲁棒性。

📝 摘要（中文）

在未知环境中定位目标物体是自主智能体的一项基本能力，应用广泛。本文提出的EffiNav方法旨在提高物体目标导航的效率，解决现有方法在泛化和效率上的不足。通过在Habitat Matterport 3D和Open-Vocabulary Object目标导航基准上进行评估，EffiNav展示了其在成功率和路径长度加权成功率上的优越性能，并在真实机器人上验证了其有效性。此外，EffiNav还可扩展至内存增强的ObjNav任务，显示出其适应性和广泛应用潜力。

🔬 方法详解

问题定义：本文解决的是在未知环境中高效定位目标物体的问题。现有方法在泛化能力和效率上存在不足，可能导致智能体在已访问区域的过度探索或冗余的往返运动。

核心思路：EffiNav通过融合深度信息和视觉语言，优化智能体的导航决策，旨在提高探索效率和路径选择的智能化。这样的设计使得智能体能够更好地理解环境并做出更有效的导航决策。

技术框架：EffiNav的整体架构包括环境感知模块、决策模块和执行模块。环境感知模块负责获取深度信息和视觉信息，决策模块基于融合的信息生成导航策略，执行模块则负责实际的移动和导航。

关键创新：EffiNav的主要创新在于其深度与视觉语言的融合方法，这种方法在提高导航效率和智能化决策方面与现有方法有本质区别。

关键设计：在设计中，EffiNav采用了特定的损失函数来平衡成功率和路径长度，同时在网络结构上进行了优化，以提高模型的训练效率和泛化能力。具体参数设置和网络结构细节在实验部分进行了详细描述。

🖼️ 关键图片

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📊 实验亮点

在Habitat Matterport 3D和Open-Vocabulary Object目标导航基准上，EffiNav在成功率和路径长度加权成功率上与最新基线相当或更优，显示出其高效性和鲁棒性。具体实验结果表明，EffiNav在多个测试场景中均表现出色，验证了其在真实环境中的有效性。

🎯 应用场景

EffiNav的研究成果在多个领域具有潜在应用价值，包括搜索与救援、无人驾驶机器人、智能家居等。通过提高自主导航的效率，EffiNav能够帮助机器人在复杂环境中更快速、准确地完成任务，未来可能推动智能体在实际应用中的广泛部署。

📄 摘要（原文）

To locate a target object while exploring the unknown environment is a fundamental capability for autonomous agents, with applications ranging from search-and-rescue to field robots. A simplified version of such task is Object Goal Navigation (ObjNav). In ObjNav, successful arrival at the target object provides a basic measure of performance; however, the efficiency of the navigation trajectory is equally important, as it indicates how intelligently the agent explores and how much time remains for subsequent tasks. In unknown environments, the key to efficient navigation lies in deciding where to explore next. While many prior works aim to address this core challenge and achieved promising performance in certain settings, recent training-based models and non-training frameworks still suffer from generalization and efficiency issues respectively, which in the worst cases can lead to excessive exploration of already-visited areas or redundant back-and-forth motion. We evaluate EffiNav on two widely used simulation benchmarks Habitat Matterport 3D (HM3D) and Open-Vocabulary Object goal Navigation (OVON), and further validate its effectiveness on physical robots in real-world settings. We conduct failure analysis on massive simulation episodes. With minimal modification, we also extend EffiNav to a memory-augmented ObjNav task on the GOAT-BENCH dataset, demonstrating its adaptability beyond standard ObjNav settings. Across two standard metrics--Success Rate (SR) and Success weighted by Path Length (SPL), EffiNav matches or outperforms recent baselines, reflecting its efficiency, robustness, and practical applicability. Recognizing the different emphases of the two datasets, the performances reveals this framework is more balanced and generalizable for efficient ObjNav.