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arXiv · 2110.02207

Waypoint Models for Instruction-guided Navigation in Continuous Environments

Jacob Krantz·Aaron Gokaslan·Dhruv Batra·Stefan Lee·Oleksandr Maksymets
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Abstract

Little inquiry has explicitly addressed the role of action spaces in language-guided visual navigation -- either in terms of its effect on navigation success or the efficiency with which a robotic agent could execute the resulting trajectory. Building on the recently released VLN-CE setting for instruction following in continuous environments, we develop a class of language-conditioned waypoint prediction networks to examine this question. We vary the expressivity of these models to explore a spectrum between low-level actions and continuous waypoint prediction. We measure task performance and estimated execution time on a profiled LoCoBot robot. We find more expressive models result in simpler, faster to execute trajectories, but lower-level actions can achieve better navigation metrics by approximating shortest paths better. Further, our models outperform prior work in VLN-CE and set a new state-of-the-art on the public leaderboard -- increasing success rate by 4% with our best model on this challenging task.

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Jacob Krantz, Aaron Gokaslan, Dhruv Batra, Stefan Lee, Oleksandr Maksymets. 2021-10-05. Waypoint Models for Instruction-guided Navigation in Continuous Environments. https://arxiv.org/abs/2110.02207

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