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arXiv · 2406.03015

Balancing Performance and Efficiency in Zero-shot Robotic Navigation

Dmytro Kuzmenko·Nadiya Shvai
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Abstract

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.

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Dmytro Kuzmenko, Nadiya Shvai. 2024-06-05. Balancing Performance and Efficiency in Zero-shot Robotic Navigation. https://doi.org/10.1007/978-3-031-81372-6_28

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