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A New Path: Scaling Vision-and-Language Navigation With Synthetic Instructions and Imitation Learning

CVPR 2023 · Vision-language and multimodal › Robot action and navigation · approach: Pretraining and data augmentation for VLN · 35 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace Speaker-Matterport synthetic instructions with higher-quality Marky-Matterport instructions (ablation)MARVAL pretraining · RxR Val-UnseenSR: +27% SR on RxRHelped
Add Speaker-Matterport instructions alongside Marky-Matterport instructions (ablation)MARVAL pretraining · RxR and R2R Val-UnseenNDTW: 60.3 -> 56.9Hurt
Add Gibson environments with Marky-generated instructions (ablation)MARVAL pretraining · RxR Val-UnseenSR: +11% SR on RxRHelped
Add SE3DS novel-view image synthesis without Gibson data (ablation)MARVAL pretraining · RxR Val-UnseenSR: +6% SR on RxRHelped
Add SE3DS novel-view image synthesis when Gibson data is already included (ablation)MARVAL pretraining · RxR Val-UnseenSR: +0% SR on RxRNo clear effect
Scale the transformer from mT5-base to mT5-large (ablation)MARVAL pretraining · RxR Val-UnseenSR: +2% SR on RxRHelped
Finetune the pretrained agent on human annotations using behavioral cloning (ablation)MARVAL · RxR Val-UnseenNDTW: 67.0 -> 70.6Helped
Add DAGGER finetuning after behavioral cloning (ablation)MARVAL · RxR Val-UnseenNDTW: 70.6 -> 70.8Helped
Self-train with Marky-generated instructions in the evaluation environments (ablation)MARVAL · RxR Test (Unseen)NDTW: 66.8 -> 68.6Helped
Use MARVAL trained on RxR-domain synthetic instructions on the R2R benchmarkMARVAL · R2R Val-UnseenSR: 64.8 vs. 68.9Hurt

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