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Research map / Vision-language and multimodal / Robot action and navigation

Interactive Grounded Language Acquisition and Generalization in a 2D World

ICLR 2018 · Vision-language and multimodal › Robot action and navigation · approach: Embodied multimodal LLM agents · 29 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Share a concept detection function between language prediction and groundingProposed model · XWorld ZS2NAV success rateHelped
Disentangle explicit language grounding from downstream computationsProposed model · XWorld ZS1NAV success rate: 84.3%Helped
Disentangle explicit language grounding from downstream computationsProposed model · XWorld ZS2NAV success rate: 85.2%Helped
Use contextual attention instead of the interpreterProposed model · XWorld ZS2NAV success rate: up to 80%Hurt
Use stacked attention instead of the interpreterProposed model · XWorld ZS2NAV success rate: up to 80%Hurt
Use contextual attention instead of the interpreterProposed model · XWorld ZS2QA accuracy: up to 35%Hurt
Use stacked attention instead of the interpreterProposed model · XWorld ZS2QA accuracy: up to 35%Hurt
Remove the QA pipeline and train only on NAV (ablation)Proposed model · XWorld NAV trainingaccumulated discounted reward per session: -0.8 to -3.0Hurt
Remove spatial attention and attend to the whole imageVIS-LSTM · XWorld NAVnavigation success rateHurt
Remove spatial attention and attend to the whole imageConcatEmbed · XWorld NAVnavigation success rateHurt

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