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Egocentric Planning for Scalable Embodied Task Achievement

NeurIPS 2023 · Multi-agent systems and games › Human-AI and goal-conditioned · approach: Embodied agent learning · 2 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
replace FILM's template-based policies with iterative egocentric planningFILM-style ALFRED agent · ALFRED Test UnseenSR: 27.8 -> 36.07Helped
use ground-truth segmentation (ablation)EPA · ALFRED Valid UnseenSR: 40.11 -> 45.13Helped
use ground-truth depth (ablation)EPA · ALFRED Valid UnseenSR: 40.11 -> 41.85Helped
use ground-truth language (ablation)EPA · ALFRED Valid UnseenSR: 40.11 -> 52.29Helped
use ground-truth segmentation, depth, and language (ablation)EPA · ALFRED Valid UnseenSR: 40.11 -> 58.33Helped
remove the initial observation phase (ablation)EPA · ALFRED Valid UnseenSR: 40.11 -> 30.75Hurt
reduce initial observations to 200 (ablation)EPA · ALFRED Valid UnseenSR: 40.11 -> 37.3Hurt
remove object-preference search strategy (ablation)EPA · ALFRED Valid UnseenSR: 40.11 -> 32.89Hurt
remove replanning-based fault recovery (ablation)EPA · ALFRED Valid UnseenSR: 40.11 -> 27.31Hurt
replace symbolic planning with blind breadth-first search (ablation)EPA · ALFRED Valid UnseenSR: 40.11 -> 3.47Hurt
reuse the existing world model and specify new goals without adding task templatesEPA · Five new ALFRED task typesSR: 82\%Helped

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