Balancing Performance and Efficiency in Zero-shot Robotic Navigation
<|reference_start|>Balancing Performance and Efficiency in Zero-shot Robotic Navigation: We present an optimization study of the Vision-Language Frontier Maps (VLFM) applied to the Object Goal Navigation task in robotics. Our work evaluates the efficiency and performance of various vision-language models, object detectors, segmentation models, and multi-modal comprehension and Visual Question Answering modules. Using the $\textit{val-mini}$ and $\textit{val}$ splits of Habitat-Matterport 3D dataset, we conduct experiments on a desktop with limited VRAM. We propose a solution that achieves a higher success rate (+1.55%) improving over the VLFM BLIP-2 baseline without substantial success-weighted path length loss while requiring $\textbf{2.3 times}$ less video memory. Our findings provide insights into balancing model performance and computational efficiency, suggesting effective deployment strategies for resource-limited environments.<|reference_end|>
@article{kuzmenko2024balancing,
    title={Balancing Performance and Efficiency in Zero-shot Robotic Navigation},
    author={Dmytro Kuzmenko and Nadiya Shvai},
    journal={arXiv preprint arXiv:2406.03015},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03015},
    primaryClass={cs.RO cs.CV}
}