  Object-goal navigation is a crucial engineering task for the community of
embodied navigation; it involves navigating to an instance of a specified
object category within unseen environments. Although extensive investigations
have been conducted on both end-to-end and modular-based, data-driven
approaches, fully enabling an agent to comprehend the environment through
perceptual knowledge and perform object-goal navigation as efficiently as
humans remains a significant challenge. Recently, large language models have
shown potential in this task, thanks to their powerful capabilities for
knowledge extraction and integration. In this study, we propose a data-driven,
modular-based approach, trained on a dataset that incorporates common-sense
knowledge of object-to-room relationships extracted from a large language
model. We utilize the multi-channel Swin-Unet architecture to conduct
multi-task learning incorporating with multimodal inputs. The results in the
Habitat simulator demonstrate that our framework outperforms the baseline by an
average of 10.6% in the efficiency metric, Success weighted by Path Length
(SPL). The real-world demonstration shows that the proposed approach can
efficiently conduct this task by traversing several rooms. For more details and
real-world demonstrations, please check our project webpage
(https://sunleyuan.github.io/ObjectNav).
