MLFM: Multi-Layered Feature Maps for Richer Language Understanding in Zero-Shot Semantic Navigation
Abstract
Recent progress in large vision-language models has driven improvements in language-based semantic navigation, where an embodied agent must reach a target object described in natural language. Yet we still lack a clear, language-focused evaluation framework to test how well agents ground the words in their instructions. We address this gap by proposing LangNav, an open-vocabulary multi-object navigation dataset with natural language goal descriptions (e.g. 'go to the red short candle on the table') and corresponding fine-grained linguistic annotations (e.g., attributes: color=red, size=short; relations: support=on). These labels enable systematic evaluation of language understanding. To evaluate on this setting, we extend multi-object navigation task setting to Language-guided Multi-Object Navigation (LaMoN), where the agent must find a sequence of goals specified using language. Furthermore, we propose Multi-Layered Feature Map (MLFM), a novel method that builds a queryable, multi-layered semantic map from pretrained vision-language features and proves effective for reasoning over fine-grained attributes and spatial relations in goal descriptions. Experiments on LangNav show that MLFM outperforms state-of-the-art zero-shot mapping-based navigation baselines.
Cite
@article{arxiv.2507.07299,
title = {MLFM: Multi-Layered Feature Maps for Richer Language Understanding in Zero-Shot Semantic Navigation},
author = {Sonia Raychaudhuri and Enrico Cancelli and Tommaso Campari and Lamberto Ballan and Manolis Savva and Angel X. Chang},
journal= {arXiv preprint arXiv:2507.07299},
year = {2025}
}