  In this paper, we present LOC-ZSON, a novel Language-driven Object-Centric
image representation for object navigation task within complex scenes. We
propose an object-centric image representation and corresponding losses for
visual-language model (VLM) fine-tuning, which can handle complex object-level
queries. In addition, we design a novel LLM-based augmentation and prompt
templates for stability during training and zero-shot inference. We implement
our method on Astro robot and deploy it in both simulated and real-world
environments for zero-shot object navigation. We show that our proposed method
can achieve an improvement of 1.38 - 13.38% in terms of text-to-image recall on
different benchmark settings for the retrieval task. For object navigation, we
show the benefit of our approach in simulation and real world, showing 5% and
16.67% improvement in terms of navigation success rate, respectively.
