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arXiv · 2209.05629

Leveraging Large (Visual) Language Models for Robot 3D Scene Understanding

William Chen·Siyi Hu·Rajat Talak·Luca Carlone
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Abstract

Abstract semantic 3D scene understanding is a problem of critical importance in robotics. As robots still lack the common-sense knowledge about household objects and locations of an average human, we investigate the use of pre-trained language models to impart common sense for scene understanding. We introduce and compare a wide range of scene classification paradigms that leverage language only (zero-shot, embedding-based, and structured-language) or vision and language (zero-shot and fine-tuned). We find that the best approaches in both categories yield $\sim 70\%$ room classification accuracy, exceeding the performance of pure-vision and graph classifiers. We also find such methods demonstrate notable generalization and transfer capabilities stemming from their use of language.

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BibTeXRIS

William Chen, Siyi Hu, Rajat Talak, Luca Carlone. 2022-09-12. Leveraging Large (Visual) Language Models for Robot 3D Scene Understanding. https://arxiv.org/abs/2209.05629

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