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Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation

NeurIPS 2020 · Vision-language and multimodal › Robot action and navigation · approach: Pretraining and data augmentation for VLN · 14 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use EGP global planning without synthetic dataBaseline agent · R2R Val UnseenSR: 43% -> 52%Helped
add synthetic training dataEGP · R2R TestSR: 48% -> 53%Helped
use EGP rather than the strongest prior architectureR4R · R4R Val UnseenCLS: 37.0 -> 44.4Helped
add all possible navigable locations rather than limiting top-K to 3 (ablation)EGP · R2R Val UnseenSR: 47 -> 52Helped
add message passing (ablation)EGP planner · R2R Val UnseenSR: 42% -> 50%Helped
use three planning channels instead of one (ablation)EGP planner · R2R Val UnseenSR: 50% -> 52%Helped
triple graph dimension instead of using multiple planning channels (ablation)EGP planner · R2R Val UnseenSR: 52 -> 49Hurt
use graph-augmented supervision instead of shortest-path supervision (ablation)EGP · R2R Val UnseenSR: 46 -> 52Helped

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