超越孤立：一个通用导航的统一基准
Beyond Isolation: A Unified Benchmark for General-Purpose Navigation
AI总结：
本文提出OmniNavBench基准，通过复合复杂性、形态通用性和演示质量三大变革，解决通用导航中跨技能协调与跨形态泛化的问题，揭示现有方法在复杂导航任务中的不足。
摘要：
通用具身代理的发展受到碎片化评估协议的阻碍，这些协议孤立导航技能并固定特定机器人形态，无法反映现实世界中代理必须在不同形态间协调多样化行为的场景。为弥合这一差距，我们引入OmniNavBench，一个用于跨技能协调和跨形态泛化的基准。OmniNavBench引入三大范式转变：（1）复合复杂性。我们提出复合指令，将来自6个类别的子任务（PointNav、VLN、ObjectNav、SocialNav、Human Following和EQA）交错嵌套，迫使代理在单个回合内切换探索、交互和社会合规。 （2）形态通用性和传感器灵活性。我们提出一个模拟平台，打破单一形态评估的依赖，使方法能在人形、四足和轮式机器人上进行泛化测试，具有模块化传感器接口和170种融合合成资产与现实世界扫描的环境。 （3）演示质量。超越最短路径算法，我们通过人工遥控整理了1779条专家轨迹，捕捉诸如探索性扫视和预见性回避等行为细节。广泛评估表明，尽管现有方法声称具有统一设计，但它们在复杂、交错的通用导航任务中表现不佳。这揭示了现有能力与现实世界部署需求之间的关键差距，突显OmniNavBench作为下一代通用导航器的测试平台。数据集、代码和排行榜可在http://omninavbench.cloud-ip.cc获取。
英文摘要：
The pursuit of general-purpose embodied agents is hindered by fragmented evaluation protocols that isolate navigation skills and fixate on specific robot morphologies, failing to reflect real-world scenarios where agents must orchestrate diverse behaviors across varying embodiments. To bridge this gap, we introduce OmniNavBench, a benchmark for cross-skill coordination and cross-embodiment generalization. OmniNavBench introduces three paradigm shifts: (1) Compositional Complexity. We propose composite instructions that interleave sub-tasks from 6 categories (PointNav, VLN, ObjectNav, SocialNav, Human Following and EQA), compelling agents to transition between exploration, interaction, and social compliance within a single episode. (2) Morphological Universality and Sensor Flexibility. We present a simulation platform that breaks the reliance on single-morphology evaluation, enabling generalization tests across humanoid, quadrupedal, and wheeled robots, with a modular sensor interface and 170 environments blending synthetic assets with real-world scans. (3) Demonstrations Quality. Moving beyond shortest-path algorithms, we curate 1779 expert trajectories via human teleoperation, capturing behavioral nuances such as exploratory glance and anticipatory avoidance. Extensive evaluations demonstrate that current methods, despite their claimed unified design, struggle with the complex, interleaved nature of general-purpose navigation. This exposes a critical disparity between existing capabilities and real-world deployment demands, underscoring OmniNavBench as a testbed for the next generation of generalist navigators. Dataset, code, and leaderboard are available at http://omninavbench.cloud-ip.cc.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
