  Visual target navigation in unknown environments is a crucial problem in
robotics. Despite extensive investigation of classical and learning-based
approaches in the past, robots lack common-sense knowledge about household
objects and layouts. Prior state-of-the-art approaches to this task rely on
learning the priors during the training and typically require significant
expensive resources and time for learning. To address this, we propose a new
framework for visual target navigation that leverages Large Language Models
(LLM) to impart common sense for object searching. Specifically, we introduce
two paradigms: (i) zero-shot and (ii) feed-forward approaches that use language
to find the relevant frontier from the semantic map as a long-term goal and
explore the environment efficiently. Our analysis demonstrates the notable
zero-shot generalization and transfer capabilities from the use of language.
Experiments on Gibson and Habitat-Matterport 3D (HM3D) demonstrate that the
proposed framework significantly outperforms existing map-based methods in
terms of success rate and generalization. Ablation analysis also indicates that
the common-sense knowledge from the language model leads to more efficient
semantic exploration. Finally, we provide a real robot experiment to verify the
applicability of our framework in real-world scenarios. The supplementary video
and code can be accessed via the following link:
https://sites.google.com/view/l3mvn.
