R2R (Room-to-Room)
Introduced by Anderson et al. in Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments
R2R is a dataset for visually-grounded natural language navigation in real buildings. The dataset requires autonomous agents to follow human-generated navigation instructions in previously unseen buildings, as illustrated in the demo above. For training, each instruction is associated with a Matterport3D Simulator trajectory. 22k instructions are available, with an average length of 29 words. There is a test evaluation server for this dataset available at EvalAI.
Source: Natural language interaction with robotsBenchmarks
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R2R
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Meta-Explore
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Room2Room
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R2R+EnvDrop
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Papers
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