Vision-and-Language Navigation: Interpreting Visually-Grounded Navigation Instructions in Real Environments
Date
2018
Authors
Anderson, Peter
Wu, Qi
Teney, Damien
Bruce, Jake
Johnson, Mark
Sunderhauf, Niko
Reid, Ian
Gould, Stephen
van den Hengel, Anton
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IEEE
Abstract
A robot that can carry out a natural-language instruction has been a dream since before the Jetsons cartoon series imagined a life of leisure mediated by a fleet of attentive robot helpers. It is a dream that remains stubbornly distant. However, recent advances in vision and language methods have made incredible progress in closely related areas. This is significant because a robot interpreting a natural-language navigation instruction on the basis of what it sees is carrying out a vision and language process that is similar to Visual Question Answering. Both tasks can be interpreted as visually grounded sequence-to-sequence translation problems, and many of the same methods are applicable. To enable and encourage the application of vision and language methods to the problem of interpreting visually-grounded navigation instructions, we present the Matter-port3D Simulator - a large-scale reinforcement learning environment based on real imagery [11]. Using this simulator, which can in future support a range of embodied vision and language tasks, we provide the first benchmark dataset for visually-grounded natural language navigation in real buildings - the Room-to-Room (R2R) dataset1.
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IEEE/CVF Conference on Computer Vision and Pattern Recognition
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Journal article
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2099-12-31