LangMap: A Human-Verified Benchmark for Hierarchical Open-Vocabulary Goal Navigation
Overview
This paper introduces LangMap, a benchmark for hierarchical open-vocabulary goal navigation enriched with human-verified semantic annotations across four levels (scene, room, region, instance). Built on HM3D, it contains over 18K tasks covering 414 object categories. The authors also propose PlaNaVid, an RGB-only baseline combining bounded diverse memory with high-level planning to achieve top-tier success rates without depth or 3D scene representations. Automated and human evaluations show improved text-to-view matching accuracy and high unique-and-correct match rates.
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“biguation failures become more prominent at finer goal levels, while long-tail categories, small objects, distant targets, timely stopping, and multi-goal completion remain challenging. Benchmark and code: https://bo-miao.github.io/LangMap”
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- arXiv Robotics — research abstractsSignalLangMap: A Human-Verified Benchmark for Hierarchical Open-Vocabulary Goal Navigation
This paper introduces LangMap, a benchmark for hierarchical open-vocabulary goal navigation enriched with human-verified semantic annotations across four levels (scene, room, region, instance). Built on HM3D, it contains over 18K tasks covering 414 object categories. The authors also propose PlaNaVid, an RGB-only baseline combining bounded diverse memory with high-level planning to achieve top-tier success rates without depth or 3D scene representations. Automated and human evaluations show improved text-to-view matching accuracy and high unique-and-correct match rates.
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