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Research map / Multi-agent systems and games / Human-AI and goal-conditioned

Learning to Map for Active Semantic Goal Navigation

ICLR 2022 · Multi-agent systems and games › Human-AI and goal-conditioned · approach: Vision-language navigation agents · 4 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use active training rather than offline trainingL2M · Occupancy predictionAcc (%): 65.2 -> 68.1Helped
Use active training rather than offline trainingL2M · Semantic predictionIoU (%): 20.1 -> 25.6Helped
Train using the proposed information-gain objective instead of BALDL2M-UpperBound · Object-goal navigationSuccess (%): 33.4 ± 1.0 -> 36.3 ± 1.0Helped
Train using the proposed information-gain objective instead of predictive entropyL2M-UpperBound · Object-goal navigationSuccess (%): 31.3 ± 1.0 -> 36.3 ± 1.0Helped
Replace the learned stop decision with an oracle stop decision (ablation)L2M-Active-UpperBound · Hard object-goal navigation episodesSuccessHelped
Replace the local policy with simulator shortest paths (ablation)L2M-Active-UpperBound · Hard object-goal navigation episodesSuccessHelped
Replace both the learned stop decision and local policy with oracle stop and simulator shortest paths (ablation)L2M-Active-UpperBound · Hard object-goal navigation episodesSuccess: over 80%Helped

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