LangMap: A Hierarchical Benchmark for Open-Vocabulary Goal Navigation
The relationships between objects and language are fundamental to meaningful communication between humans and AI, and to practically useful embodied intelligence. We introduce HieraNav, a multi-granularity, open-vocabulary goal navigation task where agents interpret natural language instructions to reach targets at four semantic levels: scene, room, region, and instance. To this end, we present Language as a Map (LangMap), a large-scale benchmark built on real-world 3D indoor scans with comprehensive human-verified annotations and tasks spanning these levels. LangMap provides region labels, discriminative region descriptions, discriminative instance descriptions covering 414 object categories, and over 18K navigation tasks. Each target features both concise and detailed descriptions, enabling evaluation across different instruction styles. LangMap achieves superior annotation quality, outperforming GOAT-Bench by 23.8% in discriminative accuracy using four times fewer words. Comprehensive evaluations of zero-shot and supervised models on LangMap reveal that richer context and memory improve success, while long-tailed, small, context-dependent, and distant goals, as well as multi-goal completion, remain challenging. HieraNav and LangMap establish a rigorous testbed for advancing language-driven embodied navigation. Project: https://bo-miao.github.io/LangMap
6983254a9084cb4f0ecb5962
Bo Miao
claimed_verified
2026-02-04T12:27:18.508Z
653e553fcbc8ae32a5fce090
/avatars/64717c34101c21f5651ad983c2b22070.svg
Bo Miao
bo-miao
user
bo-miao
6983254a9084cb4f0ecb5963
Weijia Liu
6983254a9084cb4f0ecb5964
Jun Luo
6983254a9084cb4f0ecb5965
Lachlan Shinnick
6983254a9084cb4f0ecb5966
Jian Liu
6983254a9084cb4f0ecb5967
Thomas Hamilton-Smith
6983254a9084cb4f0ecb5968
Yuhe Yang
6983254a9084cb4f0ecb5969
Zijie Wu
6983254a9084cb4f0ecb596a
Vanja Videnovic
6983254a9084cb4f0ecb596b
Feras Dayoub
6983254a9084cb4f0ecb596c
Anton van den Hengel
LangMap: A Hierarchical Benchmark for Open-Vocabulary Goal Navigation
The relationships between objects and language are fundamental to meaningful communication between humans and AI, and to practically useful embodied intelligence. We introduce HieraNav, a multi-granularity, open-vocabulary goal navigation task where agents interpret natural language instructions to reach targets at four semantic levels: scene, room, region, and instance. To this end, we present Language as a Map (LangMap), a large-scale benchmark built on real-world 3D indoor scans with comprehensive human-verified annotations and tasks spanning these levels. LangMap provides region labels, discriminative region descriptions, discriminative instance descriptions covering 414 object categories, and over 18K navigation tasks. Each target features both concise and detailed descriptions, enabling evaluation across different instruction styles. LangMap achieves superior annotation quality, outperforming GOAT-Bench by 23.8% in discriminative accuracy using four times fewer words. Comprehensive evaluations of zero-shot and supervised models on LangMap reveal that richer context and memory improve success, while long-tailed, small, context-dependent, and distant goals, as well as multi-goal completion, remain challenging. HieraNav and LangMap establish a rigorous testbed for advancing language-driven embodied navigation. Project: https://bo-miao.github.io/LangMap
HieraNav presents a multi-granularity, open-vocabulary navigation task with LangMap benchmark that enables agents to follow natural language instructions across different semantic levels in 3D environments.
multi-granularity open-vocabulary goal navigation 3D indoor scans language-driven embodied navigation zero-shot supervised models semantic levels scene room region instance