We compare performance on the validation sets of the R2R dataset: the val-seen split, consisting of the same environments as in training, and the val- Table 1 : Success rate (SR) of the vision-based full agent ("RN", using ResNet) and the non-visual agent ("no vis.", setting all visual features to zero) on the R2R dataset under different model architectures (SpeakerFollower (SF)<cite> (Fried et al., 2018b)</cite> and Self-Monitoring (SM) (Ma et al., 2019) ) and training schemes.