Topological Planning with Transformers for Vision-and-Language Navigation

• URL: http://arxiv.org/abs/2012.05292v1
• Date: Wed, 9 Dec 2020 20:02:03 GMT
• Title: Topological Planning with Transformers for Vision-and-Language Navigation
• Authors: Kevin Chen, Junshen K. Chen, Jo Chuang, Marynel V\'azquez, Silvio Savarese
• Abstract summary: We propose a modular approach to vision-and-language navigation (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. Experiments show that our method outperforms previous end-to-end approaches, generates interpretable navigation plans, and exhibits intelligent behaviors such as backtracking.
• Score: 31.64229792521241
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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