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arXiv · 2012.05292

Topological Planning with Transformers for Vision-and-Language Navigation

Kevin Chen·Junshen K. Chen·Jo Chuang·Marynel Vázquez·Silvio Savarese
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Abstract

Conventional approaches to vision-and-language navigation (VLN) are trained end-to-end but struggle to perform well in freely traversable environments. Inspired by the robotics community, we propose a modular approach to VLN using topological maps. Given a natural language instruction and topological map, our approach leverages attention mechanisms to predict a navigation plan in the map. The plan is then executed with low-level actions (e.g. forward, rotate) using a robust controller. Experiments show that our method outperforms previous end-to-end approaches, generates interpretable navigation plans, and exhibits intelligent behaviors such as backtracking.

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BibTeXRIS

Kevin Chen, Junshen K. Chen, Jo Chuang, Marynel Vázquez, Silvio Savarese. 2020-12-09. Topological Planning with Transformers for Vision-and-Language Navigation. https://arxiv.org/abs/2012.05292

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