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

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.

@misc{guan2024loczson,
      title={LOC-ZSON: Language-driven Object-Centric Zero-Shot Object Retrieval and Navigation}, 
      author={Tianrui Guan and Yurou Yang and Harry Cheng and Muyuan Lin and Richard Kim and Rajasimman Madhivanan and Arnie Sen and Dinesh Manocha},
      year={2024},
      eprint={2405.05363},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2405.05363}, 
}