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arXiv · 2405.05363

LOC-ZSON: Language-driven Object-Centric Zero-Shot Object Retrieval and Navigation

Tianrui Guan·Yurou Yang·Harry Cheng·Muyuan Lin·Richard Kim·Rajasimman Madhivanan·Arnie Sen·Dinesh Manocha
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Abstract

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.

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Tianrui Guan, Yurou Yang, Harry Cheng, Muyuan Lin, Richard Kim, Rajasimman Madhivanan, Arnie Sen, Dinesh Manocha. 2024-05-08. LOC-ZSON: Language-driven Object-Centric Zero-Shot Object Retrieval and Navigation. https://arxiv.org/abs/2405.05363

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