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Zero Experience Required: Plug & Play Modular Transfer Learning for Semantic Visual Navigation

CVPR 2022 · Vision-language and multimodal › Robot action and navigation · approach: Pretraining and data augmentation for VLN · 55 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add the view reward and view augmentation to image-goal policy training (ablation)Image-goal navigation policy · Gibson split ASucc.: 22.0 -> 29.2; 24.4 -> 29.2Helped
Add the view reward and view augmentation to image-goal policy training (ablation)Image-goal navigation policy · Gibson split ASPL: 18.8 -> 21.6; 17.3 -> 21.6Helped
Use the modular semantic-search transfer model rather than the strongest listed baselineImage-goal navigation · Gibson split ASucc.: 22.6 -> 29.2Helped
Use the modular semantic-search transfer model rather than the strongest listed baselineImage-goal navigation · Gibson split BSPL: 12.4 -> 23.6Helped
Transfer the full model without target-task interactionsSemantic navigation tasks · ObjectNav-Labelsuccess rate: 8.0 -> 11.3Helped
Transfer the full model and fine-tune on the target taskSemantic navigation tasks · ObjectNav-Labelsuccess rate: 13.9 -> 21.9Helped
Transfer the full model rather than only the observation encoder (ablation)ObjectNav-Label · ObjectNav-Labelsuccess rateHelped
Increase source-task experience (ablation)Semantic search policy · Downstream ZSEL taskssuccess rateHelped
Increase vision resolution from 128 to 256 (ablation)ImageNav source policy evaluated on ObjectNav-Label · ObjectNav-Label ZSELsuccess rateHelped

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